Data Cleaning

1.

housing <- read.csv("housing.csv", stringsAsFactors = TRUE)
# remove unique identifier
housing <- housing[, -1]
# Factor variables that should be categorical not numerical
housing$OverallQual <- as.factor(housing$OverallQual)
housing$OverallCond <- as.factor(housing$OverallCond)

summary(housing)
##    MSSubClass       MSZoning     LotFrontage        LotArea        Street    
##  Min.   : 20.0   C (all):  10   Min.   : 21.00   Min.   :  1300   Grvl:   6  
##  1st Qu.: 20.0   FV     :  65   1st Qu.: 59.00   1st Qu.:  7554   Pave:1454  
##  Median : 50.0   RH     :  16   Median : 69.00   Median :  9478              
##  Mean   : 56.9   RL     :1151   Mean   : 70.05   Mean   : 10517              
##  3rd Qu.: 70.0   RM     : 218   3rd Qu.: 80.00   3rd Qu.: 11602              
##  Max.   :190.0                  Max.   :313.00   Max.   :215245              
##                                 NA's   :259                                  
##   Alley      LotShape  LandContour  Utilities      LotConfig    LandSlope 
##  Grvl:  50   IR1:484   Bnk:  63    AllPub:1459   Corner : 263   Gtl:1382  
##  Pave:  41   IR2: 41   HLS:  50    NoSeWa:   1   CulDSac:  94   Mod:  65  
##  NA's:1369   IR3: 10   Low:  36                  FR2    :  47   Sev:  13  
##              Reg:925   Lvl:1311                  FR3    :   4             
##                                                  Inside :1052             
##                                                                           
##                                                                           
##   Neighborhood   Condition1     Condition2     BldgType      HouseStyle 
##  NAmes  :225   Norm   :1260   Norm   :1445   1Fam  :1220   1Story :726  
##  CollgCr:150   Feedr  :  81   Feedr  :   6   2fmCon:  31   2Story :445  
##  OldTown:113   Artery :  48   Artery :   2   Duplex:  52   1.5Fin :154  
##  Edwards:100   RRAn   :  26   PosN   :   2   Twnhs :  43   SLvl   : 65  
##  Somerst: 86   PosN   :  19   RRNn   :   2   TwnhsE: 114   SFoyer : 37  
##  Gilbert: 79   RRAe   :  11   PosA   :   1                 1.5Unf : 14  
##  (Other):707   (Other):  15   (Other):   2                 (Other): 19  
##   OverallQual   OverallCond    YearBuilt     YearRemodAdd    RoofStyle   
##  5      :397   5      :821   Min.   :1872   Min.   :1950   Flat   :  13  
##  6      :374   6      :252   1st Qu.:1954   1st Qu.:1967   Gable  :1141  
##  7      :319   7      :205   Median :1973   Median :1994   Gambrel:  11  
##  8      :168   8      : 72   Mean   :1971   Mean   :1985   Hip    : 286  
##  4      :116   4      : 57   3rd Qu.:2000   3rd Qu.:2004   Mansard:   7  
##  9      : 43   3      : 25   Max.   :2010   Max.   :2010   Shed   :   2  
##  (Other): 43   (Other): 28                                               
##     RoofMatl     Exterior1st   Exterior2nd    MasVnrType    MasVnrArea    
##  CompShg:1434   VinylSd:515   VinylSd:504   BrkCmn : 15   Min.   :   0.0  
##  Tar&Grv:  11   HdBoard:222   MetalSd:214   BrkFace:445   1st Qu.:   0.0  
##  WdShngl:   6   MetalSd:220   HdBoard:207   None   :864   Median :   0.0  
##  WdShake:   5   Wd Sdng:206   Wd Sdng:197   Stone  :128   Mean   : 103.7  
##  ClyTile:   1   Plywood:108   Plywood:142   NA's   :  8   3rd Qu.: 166.0  
##  Membran:   1   CemntBd: 61   CmentBd: 60                 Max.   :1600.0  
##  (Other):   2   (Other):128   (Other):136                 NA's   :8       
##  ExterQual ExterCond  Foundation  BsmtQual   BsmtCond    BsmtExposure
##  Ex: 52    Ex:   3   BrkTil:146   Ex  :121   Fa  :  45   Av  :221    
##  Fa: 14    Fa:  28   CBlock:634   Fa  : 35   Gd  :  65   Gd  :134    
##  Gd:488    Gd: 146   PConc :647   Gd  :618   Po  :   2   Mn  :114    
##  TA:906    Po:   1   Slab  : 24   TA  :649   TA  :1311   No  :953    
##            TA:1282   Stone :  6   NA's: 37   NA's:  37   NA's: 38    
##                      Wood  :  3                                      
##                                                                      
##  BsmtFinType1   BsmtFinSF1     BsmtFinType2   BsmtFinSF2        BsmtUnfSF     
##  ALQ :220     Min.   :   0.0   ALQ :  19    Min.   :   0.00   Min.   :   0.0  
##  BLQ :148     1st Qu.:   0.0   BLQ :  33    1st Qu.:   0.00   1st Qu.: 223.0  
##  GLQ :418     Median : 383.5   GLQ :  14    Median :   0.00   Median : 477.5  
##  LwQ : 74     Mean   : 443.6   LwQ :  46    Mean   :  46.55   Mean   : 567.2  
##  Rec :133     3rd Qu.: 712.2   Rec :  54    3rd Qu.:   0.00   3rd Qu.: 808.0  
##  Unf :430     Max.   :5644.0   Unf :1256    Max.   :1474.00   Max.   :2336.0  
##  NA's: 37                      NA's:  38                                      
##   TotalBsmtSF      Heating     HeatingQC CentralAir Electrical     X1stFlrSF   
##  Min.   :   0.0   Floor:   1   Ex:741    N:  95     FuseA:  94   Min.   : 334  
##  1st Qu.: 795.8   GasA :1428   Fa: 49    Y:1365     FuseF:  27   1st Qu.: 882  
##  Median : 991.5   GasW :  18   Gd:241               FuseP:   3   Median :1087  
##  Mean   :1057.4   Grav :   7   Po:  1               Mix  :   1   Mean   :1163  
##  3rd Qu.:1298.2   OthW :   2   TA:428               SBrkr:1334   3rd Qu.:1391  
##  Max.   :6110.0   Wall :   4                        NA's :   1   Max.   :4692  
##                                                                                
##    X2ndFlrSF     LowQualFinSF       GrLivArea     BsmtFullBath   
##  Min.   :   0   Min.   :  0.000   Min.   : 334   Min.   :0.0000  
##  1st Qu.:   0   1st Qu.:  0.000   1st Qu.:1130   1st Qu.:0.0000  
##  Median :   0   Median :  0.000   Median :1464   Median :0.0000  
##  Mean   : 347   Mean   :  5.845   Mean   :1515   Mean   :0.4253  
##  3rd Qu.: 728   3rd Qu.:  0.000   3rd Qu.:1777   3rd Qu.:1.0000  
##  Max.   :2065   Max.   :572.000   Max.   :5642   Max.   :3.0000  
##                                                                  
##   BsmtHalfBath        FullBath        HalfBath       BedroomAbvGr  
##  Min.   :0.00000   Min.   :0.000   Min.   :0.0000   Min.   :0.000  
##  1st Qu.:0.00000   1st Qu.:1.000   1st Qu.:0.0000   1st Qu.:2.000  
##  Median :0.00000   Median :2.000   Median :0.0000   Median :3.000  
##  Mean   :0.05753   Mean   :1.565   Mean   :0.3829   Mean   :2.866  
##  3rd Qu.:0.00000   3rd Qu.:2.000   3rd Qu.:1.0000   3rd Qu.:3.000  
##  Max.   :2.00000   Max.   :3.000   Max.   :2.0000   Max.   :8.000  
##                                                                    
##   KitchenAbvGr   KitchenQual  TotRmsAbvGrd    Functional    Fireplaces   
##  Min.   :0.000   Ex:100      Min.   : 2.000   Maj1:  14   Min.   :0.000  
##  1st Qu.:1.000   Fa: 39      1st Qu.: 5.000   Maj2:   5   1st Qu.:0.000  
##  Median :1.000   Gd:586      Median : 6.000   Min1:  31   Median :1.000  
##  Mean   :1.047   TA:735      Mean   : 6.518   Min2:  34   Mean   :0.613  
##  3rd Qu.:1.000               3rd Qu.: 7.000   Mod :  15   3rd Qu.:1.000  
##  Max.   :3.000               Max.   :14.000   Sev :   1   Max.   :3.000  
##                                               Typ :1360                  
##  FireplaceQu   GarageType   GarageYrBlt   GarageFinish   GarageCars   
##  Ex  : 24    2Types :  6   Min.   :1900   Fin :352     Min.   :0.000  
##  Fa  : 33    Attchd :870   1st Qu.:1961   RFn :422     1st Qu.:1.000  
##  Gd  :380    Basment: 19   Median :1980   Unf :605     Median :2.000  
##  Po  : 20    BuiltIn: 88   Mean   :1979   NA's: 81     Mean   :1.767  
##  TA  :313    CarPort:  9   3rd Qu.:2002                3rd Qu.:2.000  
##  NA's:690    Detchd :387   Max.   :2010                Max.   :4.000  
##              NA's   : 81   NA's   :81                                 
##    GarageArea     GarageQual  GarageCond  PavedDrive   WoodDeckSF    
##  Min.   :   0.0   Ex  :   3   Ex  :   2   N:  90     Min.   :  0.00  
##  1st Qu.: 334.5   Fa  :  48   Fa  :  35   P:  30     1st Qu.:  0.00  
##  Median : 480.0   Gd  :  14   Gd  :   9   Y:1340     Median :  0.00  
##  Mean   : 473.0   Po  :   3   Po  :   7              Mean   : 94.24  
##  3rd Qu.: 576.0   TA  :1311   TA  :1326              3rd Qu.:168.00  
##  Max.   :1418.0   NA's:  81   NA's:  81              Max.   :857.00  
##                                                                      
##   OpenPorchSF     EnclosedPorch      X3SsnPorch      ScreenPorch    
##  Min.   :  0.00   Min.   :  0.00   Min.   :  0.00   Min.   :  0.00  
##  1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:  0.00  
##  Median : 25.00   Median :  0.00   Median :  0.00   Median :  0.00  
##  Mean   : 46.66   Mean   : 21.95   Mean   :  3.41   Mean   : 15.06  
##  3rd Qu.: 68.00   3rd Qu.:  0.00   3rd Qu.:  0.00   3rd Qu.:  0.00  
##  Max.   :547.00   Max.   :552.00   Max.   :508.00   Max.   :480.00  
##                                                                     
##     PoolArea        PoolQC       Fence      MiscFeature    MiscVal        
##  Min.   :  0.000   Ex  :   2   GdPrv:  59   Gar2:   2   Min.   :    0.00  
##  1st Qu.:  0.000   Fa  :   2   GdWo :  54   Othr:   2   1st Qu.:    0.00  
##  Median :  0.000   Gd  :   3   MnPrv: 157   Shed:  49   Median :    0.00  
##  Mean   :  2.759   NA's:1453   MnWw :  11   TenC:   1   Mean   :   43.49  
##  3rd Qu.:  0.000               NA's :1179   NA's:1406   3rd Qu.:    0.00  
##  Max.   :738.000                                        Max.   :15500.00  
##                                                                           
##      MoSold           YrSold        SaleType    SaleCondition    SalePrice     
##  Min.   : 1.000   Min.   :2006   WD     :1267   Abnorml: 101   Min.   : 34900  
##  1st Qu.: 5.000   1st Qu.:2007   New    : 122   AdjLand:   4   1st Qu.:129975  
##  Median : 6.000   Median :2008   COD    :  43   Alloca :  12   Median :163000  
##  Mean   : 6.322   Mean   :2008   ConLD  :   9   Family :  20   Mean   :180921  
##  3rd Qu.: 8.000   3rd Qu.:2009   ConLI  :   5   Normal :1198   3rd Qu.:214000  
##  Max.   :12.000   Max.   :2010   ConLw  :   5   Partial: 125   Max.   :755000  
##                                  (Other):   9

After removing: ID and changing OverallCond & OverallQual to factor variables Categorical Variables: 45 Numerical Variables: 35

2.

# find and count NA's
sapply(housing, function(x) sum(is.na(x)))
##    MSSubClass      MSZoning   LotFrontage       LotArea        Street 
##             0             0           259             0             0 
##         Alley      LotShape   LandContour     Utilities     LotConfig 
##          1369             0             0             0             0 
##     LandSlope  Neighborhood    Condition1    Condition2      BldgType 
##             0             0             0             0             0 
##    HouseStyle   OverallQual   OverallCond     YearBuilt  YearRemodAdd 
##             0             0             0             0             0 
##     RoofStyle      RoofMatl   Exterior1st   Exterior2nd    MasVnrType 
##             0             0             0             0             8 
##    MasVnrArea     ExterQual     ExterCond    Foundation      BsmtQual 
##             8             0             0             0            37 
##      BsmtCond  BsmtExposure  BsmtFinType1    BsmtFinSF1  BsmtFinType2 
##            37            38            37             0            38 
##    BsmtFinSF2     BsmtUnfSF   TotalBsmtSF       Heating     HeatingQC 
##             0             0             0             0             0 
##    CentralAir    Electrical     X1stFlrSF     X2ndFlrSF  LowQualFinSF 
##             0             1             0             0             0 
##     GrLivArea  BsmtFullBath  BsmtHalfBath      FullBath      HalfBath 
##             0             0             0             0             0 
##  BedroomAbvGr  KitchenAbvGr   KitchenQual  TotRmsAbvGrd    Functional 
##             0             0             0             0             0 
##    Fireplaces   FireplaceQu    GarageType   GarageYrBlt  GarageFinish 
##             0           690            81            81            81 
##    GarageCars    GarageArea    GarageQual    GarageCond    PavedDrive 
##             0             0            81            81             0 
##    WoodDeckSF   OpenPorchSF EnclosedPorch    X3SsnPorch   ScreenPorch 
##             0             0             0             0             0 
##      PoolArea        PoolQC         Fence   MiscFeature       MiscVal 
##             0          1453          1179          1406             0 
##        MoSold        YrSold      SaleType SaleCondition     SalePrice 
##             0             0             0             0             0
# Outlier detection in SalePrice
summary(housing$SalePrice)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   34900  129975  163000  180921  214000  755000
IQR = 214000 - 129975
lower.bound = 129975 - 1.5*IQR
upper.bound = 214000 + 1.5*IQR

Variables with missing values: LotFrontage, Alley, MasVnrType, MasVnrArea, BsmtQual, BsmtCond, BsmtExposre, BsmtFinType1, BsmtFinType2, Electrical, FireplaceQu, GarageType, GarageYrBlt, GarageFinish, GarageQual, GarageCond, PoolQC, Fence, MiscFeature

Although there is a large list of variables with missing data, some of the variables that are NA’s are not “really” missing. For example we see the Garage variables all have 81 missing values, well, I think it is safe to assume that there are 81 properties that do not have garages, rather than the data actually missing. The same can be inferred for nearly all the “missing” data, though a closer look at some of the variables should be assessed before the next step. These missing variables will probaby be best handled by taking the numeric values to a value of zero, if it is determined that the home most likely doesn’t contain the feature rather than it being truly missing.

While using the IQR range to detect outliers shows quite a few outliers, looking at the data tells another story. The highest sales priced seem to be correlated with at grade living living space and finished basement living space as well as age of home and other amenities, this would suggest that the prices are reflective of the data and more importantly, important to the data to depict an accurate model. I have determined that removing any data points as outliers of Sales Price would be more detrimental than advantageous.

3.

# Add NA as a factor value
housing$Alley <- addNA(housing$Alley)
housing$BsmtQual <- addNA(housing$BsmtQual)
housing$BsmtCond <- addNA(housing$BsmtCond)
housing$BsmtExposure <- addNA(housing$BsmtExposure)
housing$BsmtFinType1 <- addNA(housing$BsmtFinType1)
housing$BsmtFinType2 <- addNA(housing$BsmtFinType2)
housing$FireplaceQu <- addNA(housing$FireplaceQu)
housing$GarageType <- addNA(housing$GarageType)
housing$GarageFinish <- addNA(housing$GarageFinish)
housing$GarageQual <- addNA(housing$GarageQual)
housing$GarageCond <- addNA(housing$GarageCond)
housing$PoolQC <- addNA(housing$PoolQC)
housing$Fence <- addNA(housing$Fence)
housing$MiscFeature <- addNA(housing$MiscFeature)

# Change relevant NA numerical values to 0
housing["GarageYrBlt"][is.na(housing["GarageYrBlt"])] <- 0

4.

# Check NA's
sapply(housing, function(x) sum(is.na(x)))
##    MSSubClass      MSZoning   LotFrontage       LotArea        Street 
##             0             0           259             0             0 
##         Alley      LotShape   LandContour     Utilities     LotConfig 
##             0             0             0             0             0 
##     LandSlope  Neighborhood    Condition1    Condition2      BldgType 
##             0             0             0             0             0 
##    HouseStyle   OverallQual   OverallCond     YearBuilt  YearRemodAdd 
##             0             0             0             0             0 
##     RoofStyle      RoofMatl   Exterior1st   Exterior2nd    MasVnrType 
##             0             0             0             0             8 
##    MasVnrArea     ExterQual     ExterCond    Foundation      BsmtQual 
##             8             0             0             0             0 
##      BsmtCond  BsmtExposure  BsmtFinType1    BsmtFinSF1  BsmtFinType2 
##             0             0             0             0             0 
##    BsmtFinSF2     BsmtUnfSF   TotalBsmtSF       Heating     HeatingQC 
##             0             0             0             0             0 
##    CentralAir    Electrical     X1stFlrSF     X2ndFlrSF  LowQualFinSF 
##             0             1             0             0             0 
##     GrLivArea  BsmtFullBath  BsmtHalfBath      FullBath      HalfBath 
##             0             0             0             0             0 
##  BedroomAbvGr  KitchenAbvGr   KitchenQual  TotRmsAbvGrd    Functional 
##             0             0             0             0             0 
##    Fireplaces   FireplaceQu    GarageType   GarageYrBlt  GarageFinish 
##             0             0             0             0             0 
##    GarageCars    GarageArea    GarageQual    GarageCond    PavedDrive 
##             0             0             0             0             0 
##    WoodDeckSF   OpenPorchSF EnclosedPorch    X3SsnPorch   ScreenPorch 
##             0             0             0             0             0 
##      PoolArea        PoolQC         Fence   MiscFeature       MiscVal 
##             0             0             0             0             0 
##        MoSold        YrSold      SaleType SaleCondition     SalePrice 
##             0             0             0             0             0

After replacing NA’s I have two variables that still contain NA’s: LotFrontage with 259 (17.7%), Electrical with 1 (.068%), MasVnrType with 8 (.54%) and MasVnrArea with 8 (.54%) of the data.

5.

#drop electrical NA rows & drop MasVnrType NA rows
library(tidyr)
housing <- housing %>% drop_na(Electrical) # 1 row
housing <- housing %>% drop_na(MasVnrType) # 8 rows

# Check row with NA's
housing[rowSums(is.na(housing)) > 0, ]
##      MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape LandContour
## 8            60       RL          NA   10382   Pave  <NA>      IR1         Lvl
## 13           20       RL          NA   12968   Pave  <NA>      IR2         Lvl
## 15           20       RL          NA   10920   Pave  <NA>      IR1         Lvl
## 17           20       RL          NA   11241   Pave  <NA>      IR1         Lvl
## 25           20       RL          NA    8246   Pave  <NA>      IR1         Lvl
## 32           20       RL          NA    8544   Pave  <NA>      IR1         Lvl
## 43           85       RL          NA    9180   Pave  <NA>      IR1         Lvl
## 44           20       RL          NA    9200   Pave  <NA>      IR1         Lvl
## 51           60       RL          NA   13869   Pave  <NA>      IR2         Lvl
## 65           60       RL          NA    9375   Pave  <NA>      Reg         Lvl
## 67           20       RL          NA   19900   Pave  <NA>      Reg         Lvl
## 77           20       RL          NA    8475   Pave  <NA>      IR1         Lvl
## 85           80       RL          NA    8530   Pave  <NA>      IR1         Lvl
## 96           60       RL          NA    9765   Pave  <NA>      IR2         Lvl
## 101          20       RL          NA   10603   Pave  <NA>      IR1         Lvl
## 105          50       RM          NA    7758   Pave  <NA>      Reg         Lvl
## 112          80       RL          NA    7750   Pave  <NA>      IR1         Lvl
## 114          20       RL          NA   21000   Pave  <NA>      Reg         Bnk
## 117          20       RL          NA   11616   Pave  <NA>      Reg         Lvl
## 121          80       RL          NA   21453   Pave  <NA>      IR1         Low
## 127         120       RL          NA    4928   Pave  <NA>      IR1         Lvl
## 132          60       RL          NA   12224   Pave  <NA>      IR1         Lvl
## 134          20       RL          NA    6853   Pave  <NA>      IR1         Lvl
## 137          20       RL          NA   10355   Pave  <NA>      IR1         Lvl
## 148          60       RL          NA    9505   Pave  <NA>      IR1         Lvl
## 150          50       RM          NA    6240   Pave  <NA>      Reg         Lvl
## 153          60       RL          NA   14803   Pave  <NA>      IR1         Lvl
## 154          20       RL          NA   13500   Pave  <NA>      Reg         Lvl
## 161          20       RL          NA   11120   Pave  <NA>      IR1         Lvl
## 167          20       RL          NA   10708   Pave  <NA>      IR1         Lvl
## 170          20       RL          NA   16669   Pave  <NA>      IR1         Lvl
## 171          50       RM          NA   12358   Pave  <NA>      IR1         Lvl
## 178          50       RL          NA   13650   Pave  <NA>      Reg         Lvl
## 181         160       FV          NA    2117   Pave  <NA>      Reg         Lvl
## 187          80       RL          NA    9947   Pave  <NA>      IR1         Lvl
## 192          60       RL          NA    7472   Pave  <NA>      IR1         Lvl
## 204         120       RM          NA    4438   Pave  <NA>      Reg         Lvl
## 208          20       RL          NA   12493   Pave  <NA>      IR1         Lvl
## 209          60       RL          NA   14364   Pave  <NA>      IR1         Low
## 215          60       RL          NA   10900   Pave  <NA>      IR1         Lvl
## 219          50       RL          NA   15660   Pave  <NA>      IR1         Lvl
## 222          60       RL          NA    8068   Pave  <NA>      IR1         Lvl
## 237          60       RL          NA    9453   Pave  <NA>      IR1         Lvl
## 244          60       RL          NA    8880   Pave  <NA>      IR1         Lvl
## 249          50       RL          NA  159000   Pave  <NA>      IR2         Low
## 269          20       RL          NA    7917   Pave  <NA>      IR1         Lvl
## 287          20       RL          NA    8125   Pave  <NA>      IR1         Lvl
## 288          20       RL          NA    9819   Pave  <NA>      IR1         Lvl
## 293          60       RL          NA   16659   Pave  <NA>      IR1         Lvl
## 307          50       RM          NA    7920   Pave  Grvl      IR1         Lvl
## 308          30       RL          NA   12342   Pave  <NA>      IR1         Lvl
## 310          60       RL          NA    7685   Pave  <NA>      IR1         Lvl
## 319          80       RL          NA   14115   Pave  <NA>      Reg         Lvl
## 328          75       RL          NA   11888   Pave  Pave      IR1         Bnk
## 330          90       RL          NA   10624   Pave  <NA>      IR1         Lvl
## 335         190       RL          NA  164660   Grvl  <NA>      IR1         HLS
## 342          90       RL          NA    8544   Pave  <NA>      Reg         Lvl
## 346          20       RL          NA   12772   Pave  <NA>      IR1         Lvl
## 347          20       RL          NA   17600   Pave  <NA>      IR1         Lvl
## 351         120       RL          NA    5271   Pave  <NA>      IR1         Low
## 356          20       RL          NA    9248   Pave  <NA>      IR1         Lvl
## 360          85       RL          NA    7540   Pave  <NA>      IR1         Lvl
## 361          50       RL          NA    9144   Pave  Pave      Reg         Lvl
## 364          60       RL          NA   18800   Pave  <NA>      IR1         Lvl
## 366          20       RL          NA    9500   Pave  <NA>      IR1         Lvl
## 369          20       RL          NA    9830   Pave  <NA>      IR1         Lvl
## 370          60       RL          NA    8121   Pave  <NA>      IR1         Lvl
## 375          30       RL          NA   10020   Pave  <NA>      IR1         Low
## 384          60       RL          NA   53107   Pave  <NA>      IR2         Low
## 392          20       RL          NA    8339   Pave  <NA>      IR1         Lvl
## 393          30       RL          NA    7446   Pave  <NA>      Reg         Lvl
## 404          60       RL          NA   10364   Pave  <NA>      IR1         Lvl
## 405          20       RL          NA    9991   Pave  <NA>      IR1         Lvl
## 412          20       FV          NA    4403   Pave  <NA>      IR2         Lvl
## 421          20       RL          NA   16635   Pave  <NA>      IR1         Lvl
## 426          80       RL          NA   12800   Pave  <NA>      Reg         Low
## 447          60       RL          NA   11214   Pave  <NA>      IR1         Lvl
## 452          60       RL          NA    9303   Pave  <NA>      IR1         Lvl
## 457          20       RL          NA   53227   Pave  <NA>      IR1         Low
## 458          70       RM          NA    5100   Pave  Grvl      Reg         Lvl
## 459          50       RL          NA    7015   Pave  <NA>      IR1         Bnk
## 465         120       RM          NA    3072   Pave  <NA>      Reg         Lvl
## 470         120       RL          NA    6820   Pave  <NA>      IR1         Lvl
## 484          20       RL          NA    7758   Pave  <NA>      IR1         Lvl
## 490         160       RM          NA    2665   Pave  <NA>      Reg         Lvl
## 496          20       RL          NA   12692   Pave  <NA>      IR1         Lvl
## 516          80       RL          NA   10448   Pave  <NA>      IR1         Lvl
## 518          60       RL          NA    9531   Pave  <NA>      IR1         Lvl
## 536          20       RL          NA   12735   Pave  <NA>      IR1         Lvl
## 537          20       RL          NA   11553   Pave  <NA>      IR1         Lvl
## 538          20       RL          NA   11423   Pave  <NA>      Reg         Lvl
## 540          60       RL          NA   11000   Pave  <NA>      Reg         Lvl
## 544          50       RL          NA   13837   Pave  <NA>      IR1         Lvl
## 558         120       RL          NA    3196   Pave  <NA>      Reg         Lvl
## 559          20       RL          NA   11341   Pave  <NA>      IR1         Lvl
## 563          60       RL          NA   13346   Pave  <NA>      IR1         Lvl
## 568          90       RL          NA    7032   Pave  <NA>      IR1         Lvl
## 579          20       RL          NA   14585   Pave  <NA>      IR1         Lvl
## 592         120       RM          NA    4435   Pave  <NA>      Reg         Lvl
## 609          60       RL          NA   11050   Pave  <NA>      Reg         Lvl
## 610          80       RL          NA   10395   Pave  <NA>      IR1         Lvl
## 611          60       RL          NA   11885   Pave  <NA>      Reg         Lvl
## 615          60       RL          NA    7861   Pave  <NA>      IR1         Lvl
## 622         160       FV          NA    2117   Pave  <NA>      Reg         Lvl
## 625          20       RL          NA   12342   Pave  <NA>      IR1         Lvl
## 640          60       FV          NA    7050   Pave  <NA>      Reg         Lvl
## 644          20       RL          NA   10530   Pave  <NA>      IR1         Lvl
## 658          60       RL          NA   12384   Pave  <NA>      Reg         Lvl
## 664          60       RL          NA   18450   Pave  <NA>      IR1         Lvl
## 666          20       RL          NA   14175   Pave  <NA>      Reg         Bnk
## 670          20       RL          NA   11250   Pave  <NA>      IR1         Lvl
## 677          20       RL          NA    9945   Pave  <NA>      IR1         Lvl
## 680         120       RL          NA    2887   Pave  <NA>      Reg         HLS
## 683         160       RL          NA    5062   Pave  <NA>      IR1         Lvl
## 685         160       FV          NA    5105   Pave  <NA>      IR2         Lvl
## 688         120       RM          NA    4426   Pave  <NA>      Reg         Lvl
## 704          20       RL          NA  115149   Pave  <NA>      IR2         Low
## 707          20       RL          NA    7162   Pave  <NA>      IR1         Lvl
## 712          60       RL          NA   13517   Pave  <NA>      IR1         Lvl
## 718         120       RL          NA    6563   Pave  <NA>      IR1         Low
## 719         120       RM          NA    4426   Pave  <NA>      Reg         Lvl
## 724          20       RL          NA   21695   Pave  <NA>      IR1         Lvl
## 732          20       RL          NA    8978   Pave  <NA>      IR1         Lvl
## 743          60       RL          NA    8963   Pave  <NA>      IR1         Lvl
## 744          60       RL          NA    8795   Pave  <NA>      IR1         Lvl
## 749          60       RL          NA    7750   Pave  <NA>      Reg         Lvl
## 755          60       RL          NA   11616   Pave  <NA>      IR1         Lvl
## 768          85       RL          NA    7252   Pave  <NA>      IR1         Lvl
## 781          85       RL          NA    9101   Pave  <NA>      IR1         Lvl
## 783          20       RL          NA    9790   Pave  <NA>      Reg         Lvl
## 787          60       RL          NA   12205   Pave  <NA>      IR1         Low
## 789          80       RL          NA   11333   Pave  <NA>      IR1         Lvl
## 792          60       RL          NA   10832   Pave  <NA>      IR1         Lvl
## 809         120       RM          NA    4438   Pave  <NA>      Reg         Lvl
## 814          20       RL          NA   11425   Pave  <NA>      IR1         Lvl
## 815          20       RL          NA   13265   Pave  <NA>      IR1         Lvl
## 820          60       RL          NA   12394   Pave  <NA>      IR1         Lvl
## 826          60       RL          NA   28698   Pave  <NA>      IR2         Low
## 838          70       RH          NA   12155   Pave  <NA>      IR1         Lvl
## 843          85       RL          NA   16647   Pave  <NA>      IR1         Lvl
## 849         120       RL          NA    3196   Pave  <NA>      Reg         Lvl
## 851          80       RL          NA   12095   Pave  <NA>      IR1         Lvl
## 853          20       RL          NA    6897   Pave  <NA>      IR1         Lvl
## 854          80       RL          NA   10970   Pave  <NA>      IR1         Low
## 857          60       RL          NA   11029   Pave  <NA>      IR1         Lvl
## 863          20       RL          NA    8750   Pave  <NA>      IR1         Lvl
## 866          60       RL          NA   14762   Pave  <NA>      IR2         Lvl
## 877          20       RL          NA    7000   Pave  <NA>      IR1         Lvl
## 880          60       RL          NA    9636   Pave  <NA>      IR1         Lvl
## 891          20       RL          NA   13284   Pave  <NA>      Reg         Lvl
## 898          20       RL          NA    7340   Pave  <NA>      IR1         Lvl
## 902          20       RL          NA    6173   Pave  <NA>      IR1         Lvl
## 906          20       RL          NA    8885   Pave  <NA>      IR1         Low
## 909          20       RL          NA    9286   Pave  <NA>      IR1         Lvl
## 915          20       RL          NA   17140   Pave  <NA>      Reg         Lvl
## 923          20       RL          NA   15611   Pave  <NA>      IR1         Lvl
## 925          60       RL          NA    9900   Pave  <NA>      Reg         Lvl
## 926          20       RL          NA   11838   Pave  <NA>      Reg         Lvl
## 927          60       RL          NA   13006   Pave  <NA>      IR1         Lvl
## 936          70       RL          NA   24090   Pave  <NA>      Reg         Lvl
## 938          60       RL          NA    8755   Pave  <NA>      IR1         Lvl
## 941          20       RL          NA   14375   Pave  <NA>      IR1         Lvl
## 950          60       RL          NA   11075   Pave  <NA>      IR1         Lvl
## 958          60       RL          NA   12227   Pave  <NA>      IR1         Lvl
## 964          20       RL          NA    7390   Pave  <NA>      IR1         Lvl
## 971         160       FV          NA    2651   Pave  <NA>      Reg         Lvl
## 975          85       RL          NA   12122   Pave  <NA>      IR1         Lvl
## 978          60       RL          NA   11250   Pave  <NA>      Reg         Lvl
## 983          60       RL          NA   12046   Pave  <NA>      IR1         Lvl
## 991          20       RL          NA   10659   Pave  <NA>      IR1         Lvl
## 992          20       RL          NA   11717   Pave  <NA>      IR1         Lvl
## 998          90       RL          NA   11500   Pave  <NA>      IR1         Lvl
## 1001         20       RL          NA   12155   Pave  <NA>      IR3         Lvl
## 1012        120       RL          NA    5814   Pave  <NA>      IR1         Lvl
## 1013         80       RL          NA   10784   Pave  <NA>      IR1         Lvl
## 1019         20       RL          NA   15498   Pave  <NA>      IR1         Lvl
## 1025        190       RH          NA    7082   Pave  <NA>      Reg         Lvl
## 1027         60       RL          NA   14541   Pave  <NA>      IR1         Lvl
## 1028         20       RL          NA    8125   Pave  <NA>      Reg         Lvl
## 1030         20       RL          NA   11500   Pave  <NA>      IR1         Lvl
## 1032         60       RL          NA    9240   Pave  <NA>      Reg         Lvl
## 1036         60       RL          NA    9130   Pave  <NA>      Reg         Lvl
## 1040         20       RL          NA   13680   Pave  <NA>      IR1         Lvl
## 1052         60       RL          NA   29959   Pave  <NA>      IR2         Lvl
## 1054         50       RL          NA   11275   Pave  <NA>      IR1         HLS
## 1059         20       RL          NA   11000   Pave  <NA>      IR1         Lvl
## 1072         20       RL          NA   15870   Pave  <NA>      IR1         Lvl
## 1079         60       RL          NA   13031   Pave  <NA>      IR2         Lvl
## 1081        160       RM          NA    1974   Pave  <NA>      Reg         Lvl
## 1092        120       RL          NA    3696   Pave  <NA>      Reg         Lvl
## 1103         60       RL          NA    8063   Pave  <NA>      Reg         Lvl
## 1105         60       RL          NA    8000   Pave  <NA>      Reg         Lvl
## 1111         80       RL          NA    7750   Pave  <NA>      Reg         Lvl
## 1117         20       RL          NA    8926   Pave  <NA>      IR1         Lvl
## 1119         80       RL          NA    9125   Pave  <NA>      IR1         Lvl
## 1133         20       RL          NA    9819   Pave  <NA>      IR1         Lvl
## 1136         60       RL          NA   10304   Pave  <NA>      IR1         Lvl
## 1138         20       RL          NA    9000   Pave  <NA>      Reg         Lvl
## 1141         20       RL          NA   11200   Pave  <NA>      Reg         Lvl
## 1143         50       RM          NA    5700   Pave  <NA>      Reg         Lvl
## 1148         30       RM          NA    5890   Pave  <NA>      Reg         Lvl
## 1149         60       RL          NA   13700   Pave  <NA>      IR1         Lvl
## 1156         20       RL          NA   14778   Pave  <NA>      IR1         Low
## 1159         80       RL          NA   16157   Pave  <NA>      IR1         Lvl
## 1172         50       RM          NA    3950   Pave  Grvl      Reg         Bnk
## 1175         60       RL          NA   11170   Pave  <NA>      IR2         Lvl
## 1185        190       RL          NA   32463   Pave  <NA>      Reg         Low
## 1188        120       RM          NA    4500   Pave  <NA>      Reg         Lvl
## 1201         20       RH          NA    8900   Pave  <NA>      Reg         Lvl
## 1208         80       RL          NA   10246   Pave  <NA>      IR1         Lvl
## 1225         90       RL          NA   18890   Pave  <NA>      IR1         Lvl
## 1228         20       RL          NA   12160   Pave  <NA>      IR1         Lvl
## 1238         70       RL          NA   11435   Pave  <NA>      IR1         HLS
## 1241         80       RL          NA   12328   Pave  <NA>      IR1         Lvl
## 1245        120       RL          NA    3136   Pave  <NA>      IR1         Lvl
## 1247         60       RL          NA   17542   Pave  <NA>      IR1         Lvl
## 1254         60       RL          NA   24682   Pave  <NA>      IR3         Lvl
## 1256         50       RL          NA   11250   Pave  <NA>      Reg         Lvl
## 1262         50       RL          NA   14100   Pave  <NA>      IR1         Lvl
## 1264         40       RL          NA   23595   Pave  <NA>      Reg         Low
## 1265         20       RL          NA    9156   Pave  <NA>      IR1         Lvl
## 1266         20       RL          NA   13526   Pave  <NA>      IR1         Lvl
## 1270         60       RL          NA   12936   Pave  <NA>      IR1         Lvl
## 1271         80       RL          NA   17871   Pave  <NA>      IR1         Lvl
## 1279         20       RL          NA    9790   Pave  <NA>      Reg         Lvl
## 1280         20       RL          NA   36500   Pave  <NA>      IR1         Low
## 1283         80       RL          NA   14112   Pave  <NA>      IR1         Lvl
## 1293         60       RL          NA   10762   Pave  <NA>      IR1         Lvl
## 1294         70       RL          NA    7500   Pave  <NA>      IR1         Bnk
## 1302         20       RL          NA    7153   Pave  <NA>      Reg         Lvl
## 1305         60       RL          NA    9572   Pave  <NA>      IR1         Lvl
## 1311         20       RL          NA   14781   Pave  <NA>      IR2         Lvl
## 1314         20       RL          NA    6627   Pave  <NA>      IR1         Lvl
## 1335         60       RL          NA    9375   Pave  <NA>      Reg         Lvl
## 1339         20       RL          NA   20781   Pave  <NA>      IR2         Lvl
## 1341         20       RL          NA   16196   Pave  <NA>      IR3         Low
## 1347         60       RL          NA   10316   Pave  <NA>      IR1         Lvl
## 1349         20       RL          NA    9477   Pave  <NA>      Reg         Lvl
## 1350         20       RL          NA   12537   Pave  <NA>      IR1         Lvl
## 1351        160       FV          NA    2117   Pave  <NA>      Reg         Lvl
## 1355         50       RL          NA   12513   Pave  <NA>      IR1         Lvl
## 1358         60       FV          NA    7500   Pave  <NA>      Reg         Lvl
## 1361        120       RM          NA    4435   Pave  <NA>      Reg         Lvl
## 1366         20       RL          NA   11400   Pave  <NA>      Reg         Lvl
## 1373         20       RL          NA   12925   Pave  <NA>      IR1         Lvl
## 1375         30       RL          NA   25339   Pave  <NA>      Reg         Lvl
## 1388         20       RL          NA   57200   Pave  <NA>      IR1         Bnk
## 1399         20       RL          NA    8780   Pave  <NA>      IR1         Lvl
## 1409         60       RL          NA   16545   Pave  <NA>      IR1         Lvl
## 1411         20       RL          NA   16381   Pave  <NA>      IR1         Lvl
## 1415         80       RL          NA   19690   Pave  <NA>      IR1         Lvl
## 1416         20       RL          NA    9503   Pave  <NA>      Reg         Lvl
## 1421         20       RL          NA   12546   Pave  <NA>      IR1         Lvl
## 1423        120       RL          NA    4928   Pave  <NA>      IR1         Lvl
## 1433        120       RM          NA    4426   Pave  <NA>      Reg         Lvl
## 1435         30       RL          NA    8854   Pave  <NA>      Reg         Lvl
## 1438         20       RL          NA   26142   Pave  <NA>      IR1         Lvl
##      Utilities LotConfig LandSlope Neighborhood Condition1 Condition2 BldgType
## 8       AllPub    Corner       Gtl       NWAmes       PosN       Norm     1Fam
## 13      AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 15      AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 17      AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 25      AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 32      AllPub   CulDSac       Gtl       Sawyer       Norm       Norm     1Fam
## 43      AllPub   CulDSac       Gtl      SawyerW       Norm       Norm     1Fam
## 44      AllPub   CulDSac       Gtl      CollgCr       Norm       Norm     1Fam
## 51      AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 65      AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 67      AllPub    Inside       Gtl        NAmes       PosA       Norm     1Fam
## 77      AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 85      AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 96      AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 101     AllPub    Inside       Gtl       NWAmes       Norm       Norm     1Fam
## 105     AllPub    Corner       Gtl       IDOTRR       Norm       Norm     1Fam
## 112     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 114     AllPub    Corner       Gtl      Crawfor       Norm       Norm     1Fam
## 117     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 121     AllPub   CulDSac       Sev      ClearCr       Norm       Norm     1Fam
## 127     AllPub    Inside       Gtl      NPkVill       Norm       Norm   TwnhsE
## 132     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 134     AllPub    Inside       Gtl       Timber       Norm       Norm     1Fam
## 137     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 148     AllPub   CulDSac       Gtl      Gilbert       Norm       Norm     1Fam
## 150     AllPub    Inside       Gtl      BrkSide       Norm       Norm     1Fam
## 153     AllPub   CulDSac       Gtl       NWAmes       Norm       Norm     1Fam
## 154     AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 161     AllPub   CulDSac       Gtl      Veenker       Norm       Norm     1Fam
## 167     AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 170     AllPub    Corner       Gtl       Timber       Norm       Norm     1Fam
## 171     AllPub    Inside       Gtl      OldTown      Feedr       Norm     1Fam
## 178     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 181     AllPub    Inside       Gtl      Somerst       Norm       Norm    Twnhs
## 187     AllPub   CulDSac       Gtl      Mitchel       Norm       Norm     1Fam
## 192     AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 204     AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 208     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 209     AllPub    Inside       Mod      SawyerW       Norm       Norm     1Fam
## 215     AllPub       FR2       Gtl      CollgCr       Norm       Norm     1Fam
## 219     AllPub    Corner       Gtl      Crawfor       Norm       Norm     1Fam
## 222     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 237     AllPub   CulDSac       Gtl      SawyerW       RRNe       Norm     1Fam
## 244     AllPub    Inside       Gtl      SawyerW       Norm       Norm     1Fam
## 249     AllPub   CulDSac       Sev      ClearCr       Norm       Norm     1Fam
## 269     AllPub    Corner       Gtl      Edwards       Norm       Norm     1Fam
## 287     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 288     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 293     AllPub    Corner       Gtl       NWAmes       PosA       Norm     1Fam
## 307     AllPub    Inside       Gtl       IDOTRR     Artery       Norm     1Fam
## 308     AllPub    Inside       Gtl      Edwards       Norm       Norm     1Fam
## 310     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 319     AllPub    Inside       Gtl       NWAmes       Norm       Norm     1Fam
## 328     AllPub    Inside       Gtl      BrkSide       PosN       Norm     1Fam
## 330     AllPub    Inside       Gtl        NAmes       Norm       Norm   Duplex
## 335     AllPub    Corner       Sev       Timber       Norm       Norm   2fmCon
## 342     AllPub    Inside       Gtl        NAmes       Norm       Norm   Duplex
## 346     AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 347     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 351     AllPub    Inside       Mod      ClearCr       Norm       Norm     1Fam
## 356     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 360     AllPub   CulDSac       Gtl      Mitchel       Norm       Norm     1Fam
## 361     AllPub    Inside       Gtl      BrkSide       Norm       Norm     1Fam
## 364     AllPub       FR2       Gtl       NWAmes       Norm       Norm     1Fam
## 366     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 369     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 370     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 375     AllPub    Inside       Sev      Edwards       Norm       Norm     1Fam
## 384     AllPub    Corner       Mod      ClearCr      Feedr       Norm     1Fam
## 392     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 393     AllPub    Corner       Gtl      BrkSide      Feedr       Norm     1Fam
## 404     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 405     AllPub    Corner       Gtl       Sawyer      Feedr       Norm     1Fam
## 412     AllPub    Inside       Gtl      Somerst       Norm       Norm     1Fam
## 421     AllPub       FR2       Gtl       NWAmes       Norm       Norm     1Fam
## 426     AllPub    Inside       Mod      SawyerW       Norm       Norm     1Fam
## 447     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 452     AllPub    Corner       Gtl       Timber       Norm       Norm     1Fam
## 457     AllPub   CulDSac       Mod      ClearCr       Norm       Norm     1Fam
## 458     AllPub    Inside       Gtl      OldTown       Norm       Norm     1Fam
## 459     AllPub    Corner       Gtl      BrkSide       Norm       Norm     1Fam
## 465     AllPub    Inside       Gtl      Blmngtn       Norm       Norm   TwnhsE
## 470     AllPub    Corner       Gtl      StoneBr       Norm       Norm   TwnhsE
## 484     AllPub    Corner       Gtl       Sawyer       Norm       Norm     1Fam
## 490     AllPub    Inside       Gtl      MeadowV       Norm       Norm   TwnhsE
## 496     AllPub    Inside       Gtl      NoRidge       Norm       Norm     1Fam
## 516     AllPub    Corner       Gtl       NWAmes       Norm       Norm     1Fam
## 518     AllPub   CulDSac       Gtl      CollgCr       Norm       Norm     1Fam
## 536     AllPub       FR2       Gtl        NAmes       Norm       Norm     1Fam
## 537     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 538     AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 540     AllPub       FR2       Gtl      NoRidge       Norm       Norm     1Fam
## 544     AllPub    Corner       Gtl       NWAmes       Norm       Norm     1Fam
## 558     AllPub    Inside       Gtl      Blmngtn       Norm       Norm   TwnhsE
## 559     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 563     AllPub   CulDSac       Gtl      NoRidge       Norm       Norm     1Fam
## 568     AllPub    Corner       Gtl        NAmes       Norm       Norm   Duplex
## 579     AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 592     AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 609     AllPub    Inside       Gtl      CollgCr       PosN       Norm     1Fam
## 610     AllPub       FR2       Gtl       NWAmes       Norm       Norm     1Fam
## 611     AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 615     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 622     AllPub    Inside       Gtl      Somerst       Norm       Norm   TwnhsE
## 625     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 640     AllPub    Inside       Gtl      Somerst       Norm       Norm     1Fam
## 644     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 658     AllPub   CulDSac       Gtl       NWAmes       Norm       Norm     1Fam
## 664     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 666     AllPub    Corner       Mod       Sawyer       Norm       Norm     1Fam
## 670     AllPub    Inside       Gtl      Veenker       Norm       Norm     1Fam
## 677     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 680     AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 683     AllPub   CulDSac       Gtl      StoneBr       Norm       Norm   TwnhsE
## 685     AllPub       FR2       Gtl      Somerst       Norm       Norm   TwnhsE
## 688     AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 704     AllPub   CulDSac       Sev      ClearCr       Norm       Norm     1Fam
## 707     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 712     AllPub   CulDSac       Gtl       Sawyer       RRAe       Norm     1Fam
## 718     AllPub   CulDSac       Mod      StoneBr       Norm       Norm     1Fam
## 719     AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 724     AllPub    Corner       Gtl      Crawfor       Norm       Norm     1Fam
## 732     AllPub    Corner       Gtl       Sawyer       Norm       Norm     1Fam
## 743     AllPub    Inside       Gtl       NWAmes       Norm       Norm     1Fam
## 744     AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 749     AllPub    Inside       Gtl      Gilbert       RRAn       Norm     1Fam
## 755     AllPub   CulDSac       Gtl       Sawyer       Norm       Norm     1Fam
## 768     AllPub   CulDSac       Gtl       Sawyer       Norm       Norm     1Fam
## 781     AllPub    Corner       Gtl      Mitchel       Norm       Norm     1Fam
## 783     AllPub    Inside       Gtl       NWAmes      Feedr       Norm     1Fam
## 787     AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 789     AllPub    Corner       Gtl      Mitchel       Norm       Norm     1Fam
## 792     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 809     AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 814     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 815     AllPub   CulDSac       Gtl      Mitchel       Norm       Norm     1Fam
## 820     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 826     AllPub   CulDSac       Sev      ClearCr       Norm       Norm     1Fam
## 838     AllPub    Inside       Gtl        SWISU       Norm       Norm     1Fam
## 843     AllPub   CulDSac       Gtl       Sawyer       RRAe       Norm     1Fam
## 849     AllPub    Inside       Gtl      Blmngtn       Norm       Norm   TwnhsE
## 851     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 853     AllPub    Corner       Gtl       Sawyer       Norm       Norm     1Fam
## 854     AllPub    Inside       Mod      CollgCr       Norm       Norm     1Fam
## 857     AllPub    Corner       Gtl       NWAmes       PosA       Norm     1Fam
## 863     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 866     AllPub    Corner       Gtl      Gilbert      Feedr       Norm     1Fam
## 877     AllPub   CulDSac       Gtl      CollgCr       Norm       Norm     1Fam
## 880     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 891     AllPub    Inside       Gtl       Sawyer       PosN       Norm     1Fam
## 898     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 902     AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 906     AllPub    Inside       Mod      Mitchel       Norm       Norm     1Fam
## 909     AllPub   CulDSac       Mod      CollgCr       Norm       Norm     1Fam
## 915     AllPub    Inside       Gtl      Edwards       Norm       Norm     1Fam
## 923     AllPub    Corner       Gtl       NWAmes       Norm       Norm     1Fam
## 925     AllPub    Inside       Gtl       NWAmes      Feedr       Norm     1Fam
## 926     AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 927     AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 936     AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 938     AllPub       FR2       Gtl      Gilbert       RRNn       Norm     1Fam
## 941     NoSeWa   CulDSac       Gtl       Timber       Norm       Norm     1Fam
## 950     AllPub    Inside       Mod      Mitchel       Norm       Norm     1Fam
## 958     AllPub    Corner       Gtl       NWAmes       PosN       Norm     1Fam
## 964     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 971     AllPub       FR2       Gtl      Somerst       Norm       Norm    Twnhs
## 975     AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 978     AllPub    Corner       Gtl      CollgCr       Norm       Norm     1Fam
## 983     AllPub    Inside       Gtl       NWAmes       Norm       Norm     1Fam
## 991     AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 992     AllPub    Inside       Gtl       NWAmes       PosA       Norm     1Fam
## 998     AllPub    Corner       Gtl       NWAmes      Feedr       RRAn   Duplex
## 1001    AllPub    Inside       Gtl        NAmes       PosN       Norm     1Fam
## 1012    AllPub   CulDSac       Gtl      StoneBr       Norm       Norm   TwnhsE
## 1013    AllPub       FR2       Gtl      Gilbert       Norm       Norm     1Fam
## 1019    AllPub    Corner       Gtl       Timber       Norm       Norm     1Fam
## 1025    AllPub    Inside       Gtl        SWISU       Norm       Norm   2fmCon
## 1027    AllPub    Corner       Gtl      NoRidge       Norm       Norm     1Fam
## 1028    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 1030    AllPub   CulDSac       Gtl      Edwards       Norm       Norm     1Fam
## 1032    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 1036    AllPub    Inside       Gtl       NWAmes      Feedr       Norm     1Fam
## 1040    AllPub   CulDSac       Gtl      Edwards       Norm       Norm     1Fam
## 1052    AllPub       FR2       Gtl      NoRidge       Norm       Norm     1Fam
## 1054    AllPub    Corner       Mod      Crawfor       Norm       Norm     1Fam
## 1059    AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 1072    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 1079    AllPub    Corner       Gtl      Gilbert       Norm       Norm     1Fam
## 1081    AllPub    Inside       Gtl      MeadowV       Norm       Norm   TwnhsE
## 1092    AllPub    Inside       Gtl      StoneBr       Norm       Norm   TwnhsE
## 1103    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 1105    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 1111    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 1117    AllPub    Corner       Gtl      Edwards       Norm       Norm     1Fam
## 1119    AllPub    Inside       Gtl      Gilbert       Norm       Norm     1Fam
## 1133    AllPub    Inside       Mod      Mitchel       Norm       Norm     1Fam
## 1136    AllPub   CulDSac       Gtl       NWAmes       PosN       Norm     1Fam
## 1138    AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 1141    AllPub    Inside       Gtl      SawyerW       Norm       Norm     1Fam
## 1143    AllPub    Inside       Gtl      OldTown       Norm       Norm     1Fam
## 1148    AllPub    Corner       Gtl       IDOTRR       Norm       Norm     1Fam
## 1149    AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 1156    AllPub   CulDSac       Gtl      Crawfor       PosN       Norm     1Fam
## 1159    AllPub       FR2       Gtl      Veenker      Feedr       Norm     1Fam
## 1172    AllPub    Inside       Gtl      OldTown     Artery       Norm     1Fam
## 1175    AllPub    Corner       Gtl       Timber       Norm       Norm     1Fam
## 1185    AllPub    Inside       Mod      Mitchel       Norm       Norm   2fmCon
## 1188    AllPub       FR2       Gtl      Mitchel       Norm       Norm   TwnhsE
## 1201    AllPub    Inside       Gtl      SawyerW       Norm       Norm     1Fam
## 1208    AllPub   CulDSac       Gtl       Sawyer       Norm       Norm     1Fam
## 1225    AllPub    Inside       Gtl       Sawyer      Feedr       RRAe   Duplex
## 1228    AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 1238    AllPub    Corner       Mod      Crawfor       Norm       Norm     1Fam
## 1241    AllPub    Inside       Gtl      Mitchel       Norm       Norm     1Fam
## 1245    AllPub    Corner       Gtl      NridgHt       Norm       Norm   TwnhsE
## 1247    AllPub    Inside       Gtl      Veenker       Norm       Norm     1Fam
## 1254    AllPub   CulDSac       Gtl      Gilbert       RRAn       Norm     1Fam
## 1256    AllPub    Inside       Gtl      ClearCr       Norm       Norm     1Fam
## 1262    AllPub    Inside       Mod      Crawfor       Norm       Norm     1Fam
## 1264    AllPub    Inside       Sev      ClearCr       Norm       Norm     1Fam
## 1265    AllPub    Inside       Gtl       NWAmes       PosN       Norm     1Fam
## 1266    AllPub   CulDSac       Gtl       Sawyer       Norm       Norm     1Fam
## 1270    AllPub   CulDSac       Gtl       NWAmes       Norm       Norm     1Fam
## 1271    AllPub   CulDSac       Gtl       NWAmes       Norm       Norm     1Fam
## 1279    AllPub    Inside       Gtl       NWAmes      Feedr       Norm     1Fam
## 1280    AllPub    Inside       Mod      ClearCr       Norm       Norm     1Fam
## 1283    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 1293    AllPub   CulDSac       Gtl      Gilbert       Norm       Norm     1Fam
## 1294    AllPub    Inside       Gtl      Crawfor       Norm       Norm     1Fam
## 1302    AllPub    Inside       Gtl      SawyerW       Norm       Norm     1Fam
## 1305    AllPub    Inside       Gtl      NoRidge       Norm       Norm     1Fam
## 1311    AllPub   CulDSac       Gtl      CollgCr       Norm       Norm     1Fam
## 1314    AllPub    Corner       Gtl      BrkSide      Feedr       Norm     1Fam
## 1335    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 1339    AllPub   CulDSac       Gtl       NWAmes       PosN       Norm     1Fam
## 1341    AllPub    Inside       Gtl      SawyerW       Norm       Norm     1Fam
## 1347    AllPub    Inside       Gtl      CollgCr       Norm       Norm     1Fam
## 1349    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 1350    AllPub   CulDSac       Gtl        NAmes       Norm       Norm     1Fam
## 1351    AllPub    Inside       Gtl      Somerst       Norm       Norm    Twnhs
## 1355    AllPub       FR2       Gtl        NAmes      Feedr       Norm     1Fam
## 1358    AllPub    Inside       Gtl      Somerst       Norm       Norm     1Fam
## 1361    AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 1366    AllPub    Inside       Gtl      NoRidge       Norm       Norm     1Fam
## 1373    AllPub    Corner       Gtl        NAmes       Norm       Norm     1Fam
## 1375    AllPub    Inside       Gtl       Sawyer       Norm       Norm     1Fam
## 1388    AllPub    Inside       Sev       Timber       Norm       Norm     1Fam
## 1399    AllPub    Corner       Gtl      Mitchel       Norm       Norm     1Fam
## 1409    AllPub    Inside       Gtl      NoRidge       Norm       Norm     1Fam
## 1411    AllPub    Inside       Gtl      Crawfor       Norm       Norm     1Fam
## 1415    AllPub   CulDSac       Gtl      Edwards       Norm       Norm     1Fam
## 1416    AllPub    Inside       Gtl        NAmes       Norm       Norm     1Fam
## 1421    AllPub    Corner       Gtl       NWAmes       Norm       Norm     1Fam
## 1423    AllPub    Inside       Gtl      NPkVill       Norm       Norm   TwnhsE
## 1433    AllPub    Inside       Gtl      CollgCr       Norm       Norm   TwnhsE
## 1435    AllPub    Inside       Gtl      BrkSide       Norm       Norm     1Fam
## 1438    AllPub   CulDSac       Gtl      Mitchel       Norm       Norm     1Fam
##      HouseStyle OverallQual OverallCond YearBuilt YearRemodAdd RoofStyle
## 8        2Story           7           6      1973         1973     Gable
## 13       1Story           5           6      1962         1962       Hip
## 15       1Story           6           5      1960         1960       Hip
## 17       1Story           6           7      1970         1970     Gable
## 25       1Story           5           8      1968         2001     Gable
## 32       1Story           5           6      1966         2006     Gable
## 43       SFoyer           5           7      1983         1983     Gable
## 44       1Story           5           6      1975         1980       Hip
## 51       2Story           6           6      1997         1997     Gable
## 65       2Story           7           5      1997         1998     Gable
## 67       1Story           7           5      1970         1989     Gable
## 77       1Story           4           7      1956         1956     Gable
## 85         SLvl           7           5      1995         1996     Gable
## 96       2Story           6           8      1993         1993     Gable
## 101      1Story           6           7      1977         2001     Gable
## 105      1.5Fin           7           4      1931         1950     Gable
## 112        SLvl           7           5      2000         2000     Gable
## 114      1Story           6           5      1953         1953       Hip
## 117      1Story           5           5      1962         1962     Gable
## 121        SLvl           6           5      1969         1969      Flat
## 127      1Story           6           5      1976         1976     Gable
## 132      2Story           6           5      2000         2000     Gable
## 134      1Story           8           5      2001         2002     Gable
## 137      1Story           5           5      1967         1967     Gable
## 148      2Story           7           5      2001         2001     Gable
## 150      1.5Fin           5           4      1936         1950     Gable
## 153      2Story           6           5      1971         1971     Gable
## 154      1Story           6           7      1960         1975      Flat
## 161      1Story           6           6      1984         1984     Gable
## 167      1Story           5           5      1955         1993       Hip
## 170      1Story           8           6      1981         1981       Hip
## 171      1.5Fin           5           6      1941         1950     Gable
## 178      1.5Fin           5           5      1958         1958     Gable
## 181      2Story           6           5      2000         2000     Gable
## 187        SLvl           7           5      1990         1991     Gable
## 192      2Story           7           9      1972         2004     Gable
## 204      1Story           6           5      2004         2004     Gable
## 208      1Story           4           5      1960         1960     Gable
## 209      2Story           7           5      1988         1989     Gable
## 215      2Story           6           7      1977         1977     Gable
## 219      1.5Fin           7           9      1939         2006     Gable
## 222      2Story           6           5      2002         2002     Gable
## 237      2Story           7           7      1993         2003     Gable
## 244      2Story           7           5      1994         2002     Gable
## 249      1.5Fin           6           7      1958         2006     Gable
## 269      1Story           6           7      1976         1976       Hip
## 287      1Story           4           4      1971         1971     Gable
## 288      1Story           5           5      1967         1967     Gable
## 293      2Story           7           7      1977         1994     Gable
## 307      1.5Fin           6           7      1920         1950     Gable
## 308      1Story           4           5      1940         1950     Gable
## 310      2Story           6           5      1993         1994     Gable
## 319        SLvl           7           5      1980         1980     Gable
## 328      2.5Unf           6           6      1916         1994     Gable
## 330      1Story           5           4      1964         1964     Gable
## 335      1.5Fin           5           6      1965         1965     Gable
## 342      1Story           3           4      1949         1950     Gable
## 346      1Story           6           8      1960         1998       Hip
## 347      1Story           6           5      1960         1960     Gable
## 351      1Story           7           5      1986         1986     Gable
## 356      1Story           6           6      1992         1992     Gable
## 360      SFoyer           6           6      1978         1978     Gable
## 361      1.5Fin           5           5      1940         1982     Gable
## 364      2Story           6           5      1976         1976     Gable
## 366      1Story           6           5      1963         1963     Gable
## 369      1Story           5           7      1959         2006     Gable
## 370      2Story           6           5      2000         2000     Gable
## 375      1Story           1           1      1922         1950     Gable
## 384      2Story           6           5      1992         1992     Gable
## 392      1Story           5           7      1959         1959     Gable
## 393      1Story           4           5      1941         1950     Gable
## 404      2Story           6           5      1995         1996     Gable
## 405      1Story           4           4      1976         1993     Gable
## 412      1Story           7           5      2009         2009     Gable
## 421      1Story           6           7      1977         2000     Gable
## 426        SLvl           7           5      1989         1989     Gable
## 447      2Story           7           5      1998         1999     Gable
## 452      2Story           6           5      1996         1997       Hip
## 457      1Story           4           6      1954         1994      Flat
## 458      2Story           8           7      1925         1996       Hip
## 459      1.5Fin           5           4      1950         1950     Gable
## 465      1Story           7           5      2004         2004       Hip
## 470      1Story           8           5      1985         1985     Gable
## 484      1Story           5           7      1962         2001     Gable
## 490      2Story           5           6      1976         1976     Gable
## 496      1Story           8           5      1992         1993       Hip
## 516        SLvl           6           6      1972         1972     Gable
## 518      2Story           6           5      1998         1998     Gable
## 536      1Story           4           5      1972         1972       Hip
## 537      1Story           5           5      1968         1968       Hip
## 538      1Story           8           5      2001         2002     Gable
## 540      2Story           8           5      2000         2000     Gable
## 544      1.5Fin           7           5      1988         1988     Gable
## 558      1Story           7           5      2003         2004     Gable
## 559      1Story           5           6      1957         1996       Hip
## 563      2Story           7           5      1992         2000     Gable
## 568      SFoyer           5           5      1979         1979     Gable
## 579      1Story           6           6      1960         1987     Gable
## 592      1Story           6           5      2003         2003     Gable
## 609      2Story           9           5      2000         2000       Hip
## 610        SLvl           6           6      1978         1978     Gable
## 611      2Story           8           5      2001         2001     Gable
## 615      2Story           6           5      2002         2003     Gable
## 622      2Story           6           5      2000         2000     Gable
## 625      1Story           5           5      1960         1978       Hip
## 640      2Story           7           5      2001         2001     Gable
## 644      1Story           6           5      1971         1971       Hip
## 658      2Story           7           7      1976         1976     Gable
## 664      2Story           6           5      1965         1979      Flat
## 666      1Story           5           6      1956         1987     Gable
## 670      1Story           6           6      1977         1977     Gable
## 677      1Story           5           5      1961         1961       Hip
## 680      1Story           6           5      1996         1997     Gable
## 683      2Story           7           5      1984         1984     Gable
## 685      2Story           7           5      2004         2004     Gable
## 688      1Story           6           5      2004         2004     Gable
## 704      1Story           7           5      1971         2002     Gable
## 707      1Story           5           7      1966         1966     Gable
## 712      2Story           6           8      1976         2005     Gable
## 718      1Story           8           5      1985         1985     Gable
## 719      1Story           6           5      2004         2004     Gable
## 724      1Story           6           9      1988         2007       Hip
## 732      1Story           5           5      1968         1968     Gable
## 743      2Story           8           9      1976         1996       Hip
## 744      2Story           7           5      2000         2000     Gable
## 749      2Story           7           5      2003         2003     Gable
## 755      2Story           6           5      1978         1978       Hip
## 768      SFoyer           5           5      1982         1982       Hip
## 781      SFoyer           5           6      1978         1978     Gable
## 783      1Story           6           5      1967         1967     Gable
## 787      2Story           6           8      1966         2007     Gable
## 789        SLvl           6           5      1976         1976     Gable
## 792      2Story           7           5      1994         1996     Gable
## 809      1Story           6           5      2004         2004     Gable
## 814      1Story           5           6      1954         1954     Gable
## 815      1Story           8           5      2002         2002       Hip
## 820      2Story           7           5      2003         2003     Gable
## 826      2Story           5           5      1967         1967      Flat
## 838      2Story           6           8      1925         1950     Gable
## 843      SFoyer           5           5      1975         1981     Gable
## 849      1Story           8           5      2003         2003     Gable
## 851        SLvl           6           6      1964         1964     Gable
## 853      1Story           5           8      1962         2010     Gable
## 854        SLvl           6           6      1978         1978     Gable
## 857      2Story           6           7      1968         1984     Gable
## 863      1Story           5           6      1970         1970     Gable
## 866      2Story           5           6      1948         1950     Gable
## 877      1Story           5           8      1978         2005     Gable
## 880      2Story           6           5      1992         1993     Gable
## 891      1Story           5           5      1954         1954     Gable
## 898      1Story           4           6      1971         1971     Gable
## 902      1Story           5           6      1967         1967     Gable
## 906      1Story           5           5      1983         1983     Gable
## 909      1Story           5           7      1977         1989     Gable
## 915      1Story           4           6      1956         1956     Gable
## 923      1Story           5           6      1977         1977     Gable
## 925      2Story           7           5      1968         1968     Gable
## 926      1Story           8           5      2001         2001       Hip
## 927      2Story           7           5      1997         1997     Gable
## 936      2Story           7           7      1940         1950     Gable
## 938      2Story           7           5      1999         1999     Gable
## 941        SLvl           6           6      1958         1958     Gable
## 950      2Story           5           4      1969         1969     Gable
## 958      2Story           6           7      1977         1995     Gable
## 964      1Story           5           7      1955         1955       Hip
## 971      2Story           7           5      2000         2000     Gable
## 975      SFoyer           7           9      1961         2007     Gable
## 978      2Story           8           5      2002         2002     Gable
## 983      2Story           6           6      1976         1976     Gable
## 991      1Story           5           6      1961         1961       Hip
## 992      1Story           6           6      1970         1970       Hip
## 998      1Story           5           6      1976         1976     Gable
## 1001     1Story           6           3      1970         1970     Gable
## 1012     1Story           8           5      1984         1984     Gable
## 1013       SLvl           7           5      1991         1992     Gable
## 1019     1Story           8           6      1976         1976       Hip
## 1025     2Story           5           8      1916         1995     Gable
## 1027     2Story           8           7      1993         1993     Gable
## 1028     1Story           7           5      2002         2002     Gable
## 1030     1Story           4           3      1957         1957     Gable
## 1032     2Story           8           5      2001         2002     Gable
## 1036     2Story           6           8      1966         2000       Hip
## 1040     1Story           3           5      1955         1955       Hip
## 1052     2Story           7           6      1994         1994     Gable
## 1054     1.5Fin           6           7      1932         1950     Gable
## 1059     1Story           5           6      1966         1966     Gable
## 1072     1Story           5           5      1969         1969     Gable
## 1079     2Story           6           5      1995         1996     Gable
## 1081     2Story           4           5      1973         1973     Gable
## 1092     1Story           8           5      1986         1986     Gable
## 1103     2Story           6           5      2000         2000     Gable
## 1105     2Story           6           5      1995         1996     Gable
## 1111       SLvl           8           5      2002         2002       Hip
## 1117     1Story           4           3      1956         1956     Gable
## 1119       SLvl           7           5      1992         1992     Gable
## 1133     1Story           6           5      1977         1977     Gable
## 1136     2Story           5           7      1976         1976     Gable
## 1138     1Story           5           3      1959         1959     Gable
## 1141     1Story           6           5      1985         1985     Gable
## 1143     1.5Fin           7           7      1926         1950     Gable
## 1148     1Story           6           8      1930         2007     Gable
## 1149     2Story           7           6      1965         1988     Gable
## 1156     1Story           6           7      1954         2006       Hip
## 1159       SLvl           5           7      1978         1978     Gable
## 1172     1.5Fin           6           8      1926         2004     Gable
## 1175     2Story           7           5      1990         1991     Gable
## 1185     1Story           4           4      1961         1975     Gable
## 1188     1Story           6           5      1999         1999       Hip
## 1201     1Story           4           4      1966         1966     Gable
## 1208       SLvl           4           9      1965         2001     Gable
## 1225     1.5Fin           5           5      1977         1977      Shed
## 1228     1Story           5           5      1959         1959       Hip
## 1238     2Story           8           7      1929         1950     Gable
## 1241       SLvl           6           5      1976         1976     Gable
## 1245     1Story           7           5      2003         2003     Gable
## 1247     2Story           7           7      1974         2003     Gable
## 1254     2Story           6           5      1999         1999     Gable
## 1256     1.5Fin           4           5      1957         1989     Gable
## 1262     1.5Fin           8           9      1935         1997     Gable
## 1264     1Story           7           6      1979         1979      Shed
## 1265     1Story           6           7      1968         1968       Hip
## 1266     1Story           5           6      1965         1965       Hip
## 1270     2Story           6           6      1972         1972     Gable
## 1271       SLvl           6           5      1967         1976     Gable
## 1279     1Story           6           5      1963         1963       Hip
## 1280     1Story           5           5      1964         1964     Gable
## 1283       SLvl           5           7      1964         1964       Hip
## 1293     2Story           7           5      1999         1999     Gable
## 1294     2Story           6           7      1942         1950     Gable
## 1302     1Story           6           5      1991         1991     Gable
## 1305     2Story           8           5      1990         1990     Gable
## 1311     1Story           8           5      2001         2002       Hip
## 1314     1Story           3           6      1949         1950       Hip
## 1335     2Story           8           5      2002         2002     Gable
## 1339     1Story           7           7      1968         2003       Hip
## 1341     1Story           7           5      1998         1998     Gable
## 1347     2Story           7           5      2000         2000     Gable
## 1349     1Story           5           5      1966         1966     Gable
## 1350     1Story           5           6      1971         2008     Gable
## 1351     2Story           6           5      2000         2000     Gable
## 1355     1.5Fin           4           4      1920         2007     Gable
## 1358     2Story           7           5      2000         2000     Gable
## 1361     1Story           6           5      2003         2004     Gable
## 1366     1Story          10           5      2001         2002       Hip
## 1373     1Story           6           7      1970         1970     Gable
## 1375     1Story           5           7      1918         2007     Gable
## 1388     1Story           5           5      1948         1950     Gable
## 1399     1Story           5           5      1985         1985     Gable
## 1409     2Story           8           5      1998         1998     Gable
## 1411     1Story           6           5      1969         1969     Gable
## 1415       SLvl           6           7      1966         1966      Flat
## 1416     1Story           5           5      1958         1983       Hip
## 1421     1Story           6           7      1981         1981     Gable
## 1423     1Story           6           6      1976         1976     Gable
## 1433     1Story           6           5      2004         2004     Gable
## 1435     1.5Unf           6           6      1916         1950     Gable
## 1438     1Story           5           7      1962         1962     Gable
##      RoofMatl Exterior1st Exterior2nd MasVnrType MasVnrArea ExterQual ExterCond
## 8     CompShg     HdBoard     HdBoard      Stone        240        TA        TA
## 13    CompShg     HdBoard     Plywood       None          0        TA        TA
## 15    CompShg     MetalSd     MetalSd    BrkFace        212        TA        TA
## 17    CompShg     Wd Sdng     Wd Sdng    BrkFace        180        TA        TA
## 25    CompShg     Plywood     Plywood       None          0        TA        Gd
## 32    CompShg     HdBoard     HdBoard       None          0        TA        TA
## 43    CompShg     HdBoard     HdBoard       None          0        TA        TA
## 44    CompShg     VinylSd     VinylSd       None          0        TA        TA
## 51    CompShg     VinylSd     VinylSd       None          0        TA        TA
## 65    CompShg     VinylSd     VinylSd    BrkFace        573        TA        TA
## 67    CompShg     Plywood     Plywood    BrkFace        287        TA        TA
## 77    CompShg     VinylSd     VinylSd       None          0        TA        TA
## 85    CompShg     HdBoard     HdBoard    BrkFace         22        TA        TA
## 96    CompShg     VinylSd     VinylSd    BrkFace         68        Ex        Gd
## 101   CompShg     Plywood     Plywood    BrkFace         28        TA        TA
## 105   CompShg      Stucco      Stucco    BrkFace        600        TA        Fa
## 112   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 114   CompShg     Wd Sdng     Wd Sdng    BrkFace        184        TA        Gd
## 117   CompShg     Wd Sdng     Wd Sdng    BrkFace        116        TA        TA
## 121     Metal     Plywood     Plywood       None          0        TA        TA
## 127   CompShg     Plywood     Plywood       None          0        TA        TA
## 132   CompShg     VinylSd     VinylSd    BrkFace         40        Gd        TA
## 134   CompShg     VinylSd     VinylSd    BrkFace        136        Gd        TA
## 137   CompShg     MetalSd     MetalSd    BrkFace        196        TA        TA
## 148   CompShg     VinylSd     VinylSd    BrkFace        180        Gd        TA
## 150   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 153   CompShg     HdBoard     HdBoard    BrkFace        252        TA        TA
## 154   CompShg     BrkFace     Plywood       None          0        TA        TA
## 161   CompShg     Plywood     Plywood       None          0        TA        TA
## 167   CompShg     Wd Sdng     Wd Sdng       None          0        Gd        TA
## 170   WdShake     Plywood     Plywood    BrkFace        653        Gd        TA
## 171   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 178   CompShg     MetalSd     MetalSd       None          0        Gd        Gd
## 181   CompShg     MetalSd     MetalSd    BrkFace        456        Gd        TA
## 187   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 192   CompShg     HdBoard     HdBoard    BrkFace        138        TA        TA
## 204   CompShg     VinylSd     VinylSd    BrkFace        205        Gd        TA
## 208   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 209   CompShg     Plywood     Plywood    BrkFace        128        Gd        TA
## 215   CompShg     HdBoard     HdBoard    BrkFace        153        TA        TA
## 219   CompShg     VinylSd     VinylSd    BrkFace        312        Gd        Gd
## 222   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 237   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 244   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 249   CompShg     Wd Sdng     HdBoard     BrkCmn        472        Gd        TA
## 269   CompShg     HdBoard     HdBoard    BrkFace        174        TA        Gd
## 287   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 288   CompShg     MetalSd     MetalSd    BrkFace         31        TA        Gd
## 293   CompShg     Plywood     Plywood    BrkFace         34        TA        TA
## 307   CompShg     MetalSd     MetalSd       None          0        TA        Fa
## 308   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 310   CompShg     HdBoard     HdBoard    BrkFace        112        TA        TA
## 319   CompShg     Plywood     Plywood    BrkFace        225        TA        TA
## 328   CompShg     Wd Sdng     Wd Shng       None          0        TA        TA
## 330   CompShg     HdBoard     HdBoard    BrkFace         84        TA        TA
## 335   CompShg     Plywood     Plywood       None          0        TA        TA
## 342   CompShg      Stucco      Stucco    BrkFace        340        TA        TA
## 346   CompShg     MetalSd     MetalSd       None          0        TA        Gd
## 347   CompShg     Wd Sdng     Wd Sdng    BrkFace         30        TA        TA
## 351   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 356   CompShg     HdBoard     HdBoard    BrkFace        106        TA        TA
## 360   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 361   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 364   CompShg     HdBoard     HdBoard    BrkFace        120        TA        TA
## 366   CompShg     Plywood     Plywood    BrkFace        247        TA        TA
## 369   CompShg     Wd Sdng     Wd Sdng       None          0        TA        Gd
## 370   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 375   CompShg     Wd Sdng     Wd Sdng       None          0        Fa        Fa
## 384   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 392   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 393   CompShg     WdShing     Wd Shng       None          0        TA        TA
## 404   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 405   CompShg     Plywood     Plywood       None          0        TA        TA
## 412   CompShg     MetalSd     MetalSd      Stone        432        Ex        TA
## 421   CompShg     CemntBd     CmentBd      Stone        126        Gd        TA
## 426   CompShg     Wd Sdng     Wd Sdng    BrkFace        145        Gd        TA
## 447   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 452   CompShg     VinylSd     VinylSd    BrkFace         42        Gd        TA
## 457   Tar&Grv     Plywood     Plywood       None          0        TA        TA
## 458   CompShg      Stucco     Wd Shng       None          0        TA        Gd
## 459   CompShg     MetalSd     MetalSd     BrkCmn        161        TA        TA
## 465   CompShg     VinylSd     VinylSd    BrkFace         18        Gd        TA
## 470   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 484   CompShg     HdBoard     Plywood       None          0        TA        Gd
## 490   CompShg     CemntBd     CmentBd       None          0        TA        TA
## 496   CompShg     BrkFace     BrkFace       None          0        Gd        TA
## 516   CompShg     HdBoard     HdBoard    BrkFace        333        TA        TA
## 518   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 536   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 537   CompShg     Plywood     Plywood    BrkFace        188        TA        TA
## 538   CompShg     VinylSd     VinylSd    BrkFace        479        Gd        TA
## 540   CompShg     VinylSd     VinylSd    BrkFace         72        Gd        TA
## 544   CompShg     HdBoard     HdBoard    BrkFace        178        Gd        Gd
## 558   CompShg     VinylSd     VinylSd    BrkFace         18        Gd        TA
## 559   CompShg     Wd Sdng     Wd Sdng    BrkFace        180        TA        TA
## 563   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 568   CompShg     MetalSd     MetalSd       None          0        TA        TA
## 579   CompShg     Wd Sdng     Wd Sdng    BrkFace         85        TA        TA
## 592   CompShg     VinylSd     VinylSd    BrkFace        170        Gd        TA
## 609   CompShg     VinylSd     VinylSd    BrkFace        204        Gd        TA
## 610   CompShg     HdBoard     HdBoard    BrkFace        233        TA        TA
## 611   CompShg     VinylSd     VinylSd    BrkFace        108        Gd        TA
## 615   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 622   CompShg     MetalSd     MetalSd    BrkFace        513        Gd        TA
## 625   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 640   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 644   CompShg     Plywood     Plywood       None          0        TA        TA
## 658   CompShg     Plywood     Plywood    BrkFace        233        TA        TA
## 664   Tar&Grv     Plywood     Plywood     BrkCmn        113        TA        Gd
## 666   CompShg     CemntBd     Wd Sdng       None          0        TA        TA
## 670   CompShg     Plywood     Plywood       None          0        Gd        TA
## 677   CompShg     Wd Sdng     Wd Sdng    BrkFace         57        TA        TA
## 680   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 683   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 685   CompShg     MetalSd     MetalSd       None          0        Gd        TA
## 688   CompShg     VinylSd     VinylSd    BrkFace        147        Gd        TA
## 704   CompShg     Plywood     Plywood      Stone        351        TA        TA
## 707   CompShg     HdBoard     HdBoard     BrkCmn         41        TA        TA
## 712   CompShg     HdBoard     Plywood    BrkFace        289        Gd        TA
## 718   CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 719   CompShg     VinylSd     VinylSd    BrkFace        169        Gd        TA
## 724   CompShg     Wd Sdng     Plywood    BrkFace        260        Gd        Gd
## 732   CompShg     Plywood     Plywood       None          0        TA        TA
## 743   CompShg     VinylSd     VinylSd    BrkFace        289        Ex        Gd
## 744   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 749   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 755   CompShg     HdBoard     HdBoard     BrkCmn        328        TA        TA
## 768   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 781   CompShg     Plywood     Plywood    BrkFace        104        TA        Gd
## 783   CompShg     BrkFace     Wd Sdng       None          0        TA        TA
## 787   CompShg     HdBoard     HdBoard    BrkFace        157        TA        TA
## 789   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 792   CompShg     MetalSd     MetalSd       None          0        Gd        TA
## 809   CompShg     VinylSd     VinylSd    BrkFace        169        Gd        TA
## 814   CompShg     BrkFace     BrkFace       None          0        TA        TA
## 815   CompShg     CemntBd     CmentBd    BrkFace        148        Gd        TA
## 820   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 826   Tar&Grv     Plywood     Plywood       None          0        TA        TA
## 838   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 843   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 849   CompShg     VinylSd     VinylSd    BrkFace         40        Gd        TA
## 851   CompShg     MetalSd     HdBoard    BrkFace        115        TA        Gd
## 853   CompShg     HdBoard     HdBoard       None          0        TA        Gd
## 854   CompShg     Plywood     HdBoard       None          0        TA        TA
## 857   CompShg     HdBoard     HdBoard    BrkFace        220        TA        TA
## 863   CompShg     MetalSd     MetalSd    BrkFace         76        TA        TA
## 866   CompShg     Plywood     Plywood       None          0        TA        TA
## 877   CompShg     VinylSd     VinylSd    BrkFace         90        Gd        Gd
## 880   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 891   CompShg     Wd Sdng     Plywood       None          0        TA        TA
## 898   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 902   CompShg     HdBoard     Wd Sdng    BrkFace         75        TA        TA
## 906   CompShg     HdBoard     HdBoard       None          0        TA        TA
## 909   CompShg     HdBoard     Plywood       None          0        TA        TA
## 915   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 923   CompShg     VinylSd     VinylSd       None          0        TA        TA
## 925   CompShg     MetalSd     MetalSd    BrkFace        342        TA        TA
## 926   CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 927   CompShg     HdBoard     HdBoard    BrkFace        285        TA        TA
## 936   CompShg     MetalSd     MetalSd       None          0        TA        Gd
## 938   CompShg     VinylSd     VinylSd    BrkFace        298        Gd        TA
## 941   CompShg     HdBoard     HdBoard    BrkFace        541        TA        TA
## 950   CompShg     HdBoard     HdBoard    BrkFace        232        TA        TA
## 958   CompShg     HdBoard     HdBoard    BrkFace        424        TA        Gd
## 964   CompShg     Wd Sdng     Wd Sdng    BrkFace        151        TA        TA
## 971   CompShg     MetalSd     MetalSd       None          0        Gd        TA
## 975   CompShg     CemntBd     CmentBd      Stone        210        Ex        TA
## 978   CompShg     CemntBd     CmentBd       None          0        Gd        TA
## 983   CompShg     Plywood     Plywood    BrkFace        298        TA        TA
## 991   CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 992   CompShg     HdBoard     HdBoard    BrkFace        571        TA        TA
## 998   CompShg     VinylSd     VinylSd    BrkFace        164        TA        TA
## 1001  CompShg     Plywood     Plywood       None          0        TA        TA
## 1012  CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 1013  CompShg     HdBoard     HdBoard    BrkFace         76        Gd        TA
## 1019  WdShake       Stone     HdBoard       None          0        Gd        TA
## 1025  CompShg     VinylSd     VinylSd       None          0        TA        TA
## 1027  CompShg     MetalSd     MetalSd       None          0        Gd        Gd
## 1028  CompShg     VinylSd     VinylSd      Stone        295        Gd        TA
## 1030  CompShg     Wd Sdng     Wd Sdng       None          0        TA        Gd
## 1032  CompShg     VinylSd     VinylSd    BrkFace        396        Gd        TA
## 1036  CompShg     HdBoard     HdBoard    BrkFace        252        TA        TA
## 1040  CompShg     BrkFace     Wd Sdng       None          0        TA        TA
## 1052  CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 1054  CompShg     MetalSd     MetalSd    BrkFace        480        TA        TA
## 1059  CompShg     Plywood     Plywood    BrkFace        200        TA        TA
## 1072  CompShg     VinylSd     Plywood       None          0        TA        TA
## 1079  CompShg     HdBoard     HdBoard       None          0        TA        TA
## 1081  CompShg     CemntBd     CmentBd       None          0        TA        TA
## 1092  CompShg     HdBoard     HdBoard       None          0        Gd        TA
## 1103  CompShg     VinylSd     VinylSd       None          0        TA        TA
## 1105  CompShg     HdBoard     HdBoard       None          0        TA        TA
## 1111  CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 1117  CompShg     AsbShng     AsbShng       None          0        TA        TA
## 1119  CompShg     HdBoard     HdBoard    BrkFace        170        TA        TA
## 1133  CompShg     Plywood     ImStucc       None          0        TA        TA
## 1136  CompShg     Plywood     Plywood    BrkFace         44        TA        Gd
## 1138  CompShg     Wd Sdng     Plywood       None          0        TA        TA
## 1141  CompShg     Wd Sdng     Wd Shng    BrkFace         85        Gd        TA
## 1143  CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 1148  CompShg     Wd Sdng     Wd Sdng       None          0        Gd        Gd
## 1149  CompShg     VinylSd     VinylSd      Stone        288        TA        TA
## 1156  CompShg     HdBoard     HdBoard    BrkFace         72        Gd        TA
## 1159  CompShg     Plywood     Plywood       None          0        TA        TA
## 1172  CompShg     MetalSd     MetalSd       None          0        TA        TA
## 1175  CompShg     MetalSd     MetalSd       None          0        TA        TA
## 1185  CompShg     MetalSd     MetalSd      Stone        149        TA        Gd
## 1188  CompShg     VinylSd     VinylSd    BrkFace        425        TA        TA
## 1201  CompShg     HdBoard     HdBoard       None          0        TA        TA
## 1208  CompShg     VinylSd     VinylSd       None          0        TA        Gd
## 1225  CompShg     Plywood     Plywood       None          1        TA        TA
## 1228  CompShg     Plywood     Plywood    BrkFace        180        TA        TA
## 1238  CompShg     BrkFace      Stucco       None          0        TA        TA
## 1241  CompShg     HdBoard     HdBoard    BrkFace        335        TA        TA
## 1245  CompShg     VinylSd     Wd Shng      Stone        163        Gd        TA
## 1247  CompShg     Wd Sdng     Wd Sdng       None          0        Gd        TA
## 1254  CompShg     VinylSd     VinylSd       None          0        TA        TA
## 1256  CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 1262  CompShg      Stucco      Stucco    BrkFace        632        TA        Gd
## 1264  WdShake     Plywood     Plywood       None          0        Gd        TA
## 1265  CompShg     BrkFace     BrkFace       None          0        TA        TA
## 1266  CompShg     HdBoard     Plywood    BrkFace        114        TA        TA
## 1270  CompShg     HdBoard     Plywood       None          0        TA        TA
## 1271  CompShg     HdBoard     HdBoard    BrkFace        359        TA        TA
## 1279  CompShg     HdBoard     HdBoard    BrkFace        451        TA        TA
## 1280  CompShg     Wd Sdng     Wd Sdng     BrkCmn        621        TA        Gd
## 1283  CompShg     Wd Sdng     HdBoard    BrkFace         86        TA        TA
## 1293  CompShg     VinylSd     VinylSd       None        344        Gd        TA
## 1294  CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 1302  CompShg     HdBoard     HdBoard    BrkFace         88        TA        TA
## 1305  CompShg     Wd Sdng     Wd Sdng    BrkFace        336        Gd        TA
## 1311  CompShg     VinylSd     VinylSd    BrkFace        178        Gd        TA
## 1314  CompShg     VinylSd     VinylSd       None          0        TA        TA
## 1335  CompShg     VinylSd     VinylSd    BrkFace        149        Gd        TA
## 1339  CompShg     BrkFace     HdBoard       None          0        TA        TA
## 1341  CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 1347  CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 1349  CompShg     HdBoard     HdBoard    BrkFace         65        TA        TA
## 1350  CompShg     VinylSd     VinylSd       None          0        TA        TA
## 1351  CompShg     MetalSd     MetalSd    BrkFace        216        Gd        TA
## 1355  CompShg     VinylSd     VinylSd       None          0        TA        Gd
## 1358  CompShg     VinylSd     VinylSd       None          0        Gd        TA
## 1361  CompShg     VinylSd     VinylSd    BrkFace        170        Gd        TA
## 1366  CompShg     VinylSd     VinylSd    BrkFace        705        Ex        TA
## 1373  CompShg     BrkFace     Plywood       None          0        TA        TA
## 1375  CompShg     Wd Sdng     Wd Sdng       None          0        TA        Gd
## 1388  CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 1399  CompShg     HdBoard     Plywood       None          0        TA        TA
## 1409  CompShg     VinylSd     VinylSd    BrkFace        731        Gd        TA
## 1411  CompShg     Plywood     Plywood    BrkFace        312        Gd        Gd
## 1415  Tar&Grv     Plywood     Plywood       None          0        Gd        Gd
## 1416  CompShg     HdBoard     HdBoard       None          0        TA        TA
## 1421  CompShg     MetalSd     MetalSd    BrkFace        310        Gd        Gd
## 1423  CompShg     Plywood     Plywood       None          0        TA        TA
## 1433  CompShg     VinylSd     VinylSd    BrkFace        147        Gd        TA
## 1435  CompShg     Wd Sdng     Wd Sdng       None          0        TA        TA
## 1438  CompShg     HdBoard     HdBoard    BrkFace        189        TA        TA
##      Foundation BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1
## 8        CBlock       Gd       TA           Mn          ALQ        859
## 13       CBlock       TA       TA           No          ALQ        737
## 15       CBlock       TA       TA           No          BLQ        733
## 17       CBlock       TA       TA           No          ALQ        578
## 25       CBlock       TA       TA           Mn          Rec        188
## 32       CBlock       TA       TA           No          Unf          0
## 43       CBlock       Gd       TA           Av          ALQ        747
## 44       CBlock       Gd       TA           Av          LwQ        280
## 51        PConc       Gd       TA           Av          GLQ        182
## 65        PConc       Gd       TA           No          GLQ        739
## 67       CBlock       Gd       TA           Gd          GLQ        912
## 77       CBlock       TA       TA           No          ALQ        228
## 85        PConc       Gd       TA           No          Unf          0
## 96        PConc       Gd       Gd           No          ALQ        310
## 101       PConc       TA       TA           Mn          ALQ       1200
## 105       PConc       TA       TA           No          LwQ        224
## 112       PConc       Gd       TA           No          GLQ        250
## 114      CBlock       Gd       TA           Mn          ALQ         35
## 117      CBlock       TA       TA           No          LwQ        170
## 121      CBlock       TA       TA           Gd          ALQ        938
## 127      CBlock       Gd       TA           No          ALQ        120
## 132       PConc       Gd       TA           No          GLQ        695
## 134       PConc       Ex       TA           No          GLQ       1005
## 137      CBlock       TA       TA           No          BLQ        695
## 148       PConc       Gd       TA           No          Unf          0
## 150      BrkTil       Gd       TA           No          Unf          0
## 153      CBlock       TA       TA           No          Rec        416
## 154      CBlock       Gd       TA           Gd          BLQ        429
## 161       PConc       Gd       TA           No          BLQ        660
## 167      CBlock       TA       TA           No          LwQ        379
## 170      CBlock       Gd       TA           No          Unf          0
## 171      CBlock       TA       TA           No          Rec        360
## 178      CBlock       TA       TA           No          ALQ         57
## 181       PConc       Gd       TA           No          GLQ        436
## 187       PConc       Gd       TA           Av          GLQ        611
## 192      CBlock       TA       TA           No          ALQ        626
## 204       PConc       Gd       TA           Av          GLQ        662
## 208       PConc       TA       TA           No          ALQ        419
## 209      CBlock       Gd       TA           Gd          GLQ       1065
## 215      CBlock       Gd       TA           No          GLQ        378
## 219      CBlock       TA       TA           No          BLQ        341
## 222       PConc       Gd       TA           No          Unf          0
## 237       PConc       Gd       TA           No          BLQ        402
## 244       PConc       Gd       TA           No          GLQ        695
## 249      CBlock       Gd       TA           Gd          Rec        697
## 269      CBlock       TA       Gd           No          BLQ        751
## 287      CBlock       TA       TA           No          BLQ        614
## 288      CBlock       TA       TA           No          BLQ        450
## 293      CBlock       TA       TA           No          ALQ        795
## 307      CBlock       TA       TA           No          Unf          0
## 308      CBlock       TA       TA           No          BLQ        262
## 310       PConc       Gd       TA           No          ALQ        518
## 319      CBlock       Gd       TA           Av          GLQ       1036
## 328      BrkTil       TA       TA           No          Unf          0
## 330      CBlock       TA       TA           No          GLQ         40
## 335      CBlock       TA       TA           Gd          ALQ       1249
## 342        Slab     <NA>     <NA>         <NA>         <NA>          0
## 346      CBlock       TA       TA           Mn          BLQ        498
## 347      CBlock       TA       TA           No          BLQ       1270
## 351       PConc       Gd       TA           Gd          GLQ       1082
## 356       PConc       Gd       TA           No          GLQ        560
## 360      CBlock       Gd       TA           Av          GLQ        773
## 361      CBlock       TA       TA           No          Rec        399
## 364       PConc       Gd       TA           Mn          GLQ        712
## 366      CBlock       Gd       TA           No          BLQ        609
## 369      CBlock       TA       TA           No          ALQ         72
## 370       PConc       Gd       TA           No          Unf          0
## 375      BrkTil       Fa       Po           Gd          BLQ        350
## 384       PConc       Gd       TA           Av          GLQ        985
## 392        Slab     <NA>     <NA>         <NA>         <NA>          0
## 393      CBlock       TA       TA           No          Rec        266
## 404       PConc       Gd       TA           No          Unf          0
## 405      CBlock       TA       TA           No          BLQ       1116
## 412       PConc       Ex       TA           Av          GLQ        578
## 421      CBlock       Gd       TA           No          ALQ       1246
## 426       PConc       Gd       TA           Gd          GLQ       1518
## 447       PConc       Gd       TA           No          Unf          0
## 452       PConc       Ex       TA           No          ALQ        742
## 457      CBlock       Gd       TA           Gd          BLQ       1116
## 458       PConc       TA       TA           No          Unf          0
## 459      CBlock       TA       TA           No          LwQ        185
## 465       PConc       Gd       TA           No          Unf          0
## 470       PConc       Gd       TA           Av          GLQ        368
## 484      CBlock       TA       TA           No          ALQ        588
## 490       PConc       Gd       TA           Mn          Unf          0
## 496       PConc       Gd       TA           No          GLQ       1231
## 516      CBlock       TA       TA           No          Unf          0
## 518       PConc       Gd       TA           Mn          GLQ        706
## 536      CBlock       TA       TA           No          BLQ        600
## 537      CBlock       TA       TA           No          BLQ        673
## 538       PConc       Gd       TA           Av          GLQ       1358
## 540       PConc       Gd       TA           No          Unf          0
## 544       PConc       Gd       Gd           No          GLQ       1002
## 558       PConc       Gd       TA           Gd          Unf          0
## 559      CBlock       Gd       TA           No          ALQ       1302
## 563       PConc       Gd       TA           No          GLQ        728
## 568      CBlock       Gd       TA           Gd          GLQ        943
## 579      CBlock       TA       TA           No          BLQ        594
## 592       PConc       Gd       TA           Av          GLQ        685
## 609       PConc       Ex       TA           Mn          GLQ        904
## 610      CBlock       Gd       TA           Av          ALQ        605
## 611       PConc       Gd       TA           Av          GLQ        990
## 615       PConc       Gd       TA           No          GLQ        457
## 622       PConc       Gd       TA           No          GLQ        420
## 625      CBlock       TA       TA           No          Unf          0
## 640       PConc       Gd       TA           No          GLQ        738
## 644      CBlock       TA       TA           No          ALQ        282
## 658      CBlock       Gd       TA           No          Unf          0
## 664      CBlock       Gd       TA           No          LwQ        187
## 666      CBlock       TA       TA           No          Rec        988
## 670      CBlock       Gd       TA           No          ALQ        767
## 677      CBlock       TA       TA           No          Rec        827
## 680       PConc       Gd       TA           Mn          GLQ       1003
## 683      CBlock       Gd       TA           Mn          GLQ        828
## 685       PConc       Gd       TA           No          GLQ        239
## 688       PConc       Gd       TA           Gd          GLQ        697
## 704      CBlock       Gd       TA           Gd          GLQ       1219
## 707       PConc       TA       TA           No          Unf          0
## 712      CBlock       TA       TA           No          GLQ        533
## 718       PConc       Gd       TA           Gd          GLQ       1148
## 719       PConc       Gd       TA           Av          GLQ        662
## 724      CBlock       Gd       TA           No          GLQ        808
## 732       PConc       TA       TA           No          Unf          0
## 743      CBlock       TA       Gd           No          GLQ        575
## 744       PConc       Gd       TA           No          GLQ        300
## 749       PConc       Gd       TA           No          Unf          0
## 755      CBlock       TA       TA           Mn          Rec        438
## 768      CBlock       Gd       TA           Av          GLQ        685
## 781       PConc       Gd       TA           Av          GLQ       1097
## 783      CBlock       TA       TA           No          Rec        251
## 787      CBlock       TA       Fa           Gd          LwQ        568
## 789       PConc       Gd       TA           Av          ALQ        539
## 792       PConc       Gd       TA           No          Unf          0
## 809       PConc       Gd       TA           Gd          GLQ        662
## 814      CBlock       TA       TA           No          BLQ        486
## 815       PConc       Gd       TA           No          GLQ       1218
## 820       PConc       Gd       TA           Gd          Unf          0
## 826       PConc       TA       Gd           Gd          LwQ        249
## 838      BrkTil       TA       TA           No          BLQ        156
## 843      CBlock       Gd       TA           Gd          ALQ       1390
## 849       PConc       Gd       TA           Gd          Unf          0
## 851      CBlock       TA       TA           Gd          Rec        564
## 853      CBlock       TA       TA           No          ALQ        659
## 854      CBlock       Gd       Gd           Gd          GLQ        505
## 857      CBlock       TA       TA           Mn          BLQ        619
## 863      CBlock       TA       TA           No          BLQ        828
## 866        Slab     <NA>     <NA>         <NA>         <NA>          0
## 877      CBlock       TA       TA           No          ALQ        646
## 880       PConc       Gd       TA           No          Unf          0
## 891       PConc       Gd       TA           Mn          BLQ       1064
## 898      CBlock       TA       TA           No          ALQ        322
## 902      CBlock       TA       TA           No          GLQ        599
## 906      CBlock       Gd       TA           Av          BLQ        301
## 909      CBlock       Gd       Gd           Av          ALQ        196
## 915      CBlock       TA       TA           No          ALQ       1059
## 923       PConc       Gd       TA           Av          ALQ        767
## 925      CBlock       TA       TA           No          BLQ        552
## 926       PConc       Gd       TA           Av          Unf          0
## 927       PConc       Gd       TA           No          Unf          0
## 936      CBlock       TA       TA           Mn          Unf          0
## 938       PConc       Gd       TA           No          ALQ        772
## 941      CBlock       TA       TA           No          GLQ        111
## 950      CBlock       TA       TA           Av          ALQ        562
## 958      CBlock       Gd       Gd           No          ALQ        896
## 964      CBlock       TA       TA           No          ALQ        902
## 971       PConc       Gd       TA           No          GLQ        641
## 975      CBlock       TA       TA           Av          ALQ        867
## 978       PConc       Gd       TA           Mn          Unf          0
## 983      CBlock       TA       TA           No          LwQ        156
## 991      CBlock       TA       TA           No          Rec        915
## 992      CBlock       TA       TA           No          Unf          0
## 998      CBlock       TA       TA           No          Unf          0
## 1001     CBlock       Gd       TA           No          LwQ       1237
## 1012     CBlock       Gd       TA           Av          GLQ       1036
## 1013      PConc       Gd       TA           No          Unf          0
## 1019     CBlock       Gd       TA           Av          ALQ       1165
## 1025      PConc       TA       TA           Mn          Unf          0
## 1027      PConc       Gd       Gd           No          GLQ       1012
## 1028      PConc       Gd       TA           No          GLQ        986
## 1030       Slab     <NA>     <NA>         <NA>         <NA>          0
## 1032      PConc       Gd       TA           No          Unf          0
## 1036     CBlock       TA       TA           No          GLQ        400
## 1040       Slab     <NA>     <NA>         <NA>         <NA>          0
## 1052      PConc       Gd       TA           No          GLQ        595
## 1054     CBlock       TA       TA           Mn          Rec        297
## 1059     CBlock       TA       TA           Mn          BLQ        740
## 1072     CBlock       TA       TA           Mn          BLQ         75
## 1079      PConc       Gd       TA           No          ALQ        592
## 1081     CBlock       TA       TA           No          BLQ        334
## 1092     CBlock       Gd       TA           No          Unf          0
## 1103      PConc       Gd       TA           No          Unf          0
## 1105      PConc       Gd       TA           No          GLQ        219
## 1111      PConc       Gd       TA           No          GLQ        353
## 1117     CBlock       TA       TA           No          Unf          0
## 1119      PConc       Gd       TA           No          Unf          0
## 1133      PConc       TA       TA           Gd          ALQ       1567
## 1136     CBlock       TA       TA           No          ALQ        381
## 1138     CBlock       TA       TA           No          GLQ        288
## 1141     CBlock       Gd       TA           No          GLQ       1258
## 1143      PConc       TA       TA           No          Unf          0
## 1148     BrkTil       TA       TA           Av          ALQ        538
## 1149     CBlock       TA       TA           Gd          ALQ        454
## 1156     CBlock       TA       TA           No          BLQ        728
## 1159      PConc       Gd       TA           Gd          ALQ        680
## 1172     CBlock       TA       TA           No          Rec        468
## 1175       Wood       Gd       TA           No          LwQ       1216
## 1185     CBlock       TA       TA           Av          BLQ       1159
## 1188      PConc       Ex       TA           No          GLQ        883
## 1201     CBlock       TA       TA           No          Rec       1056
## 1208     CBlock       TA       Gd           Av          GLQ        648
## 1225     CBlock       Gd       TA           No          GLQ        498
## 1228     CBlock       TA       TA           No          Rec       1000
## 1238      PConc       Gd       TA           No          Unf          0
## 1241     CBlock       TA       TA           Av          GLQ        539
## 1245      PConc       Gd       TA           No          Unf          0
## 1247     CBlock       TA       TA           Gd          LwQ        125
## 1254      PConc       Gd       TA           No          Unf          0
## 1256     CBlock       TA       TA           Av          Unf          0
## 1262     CBlock       TA       TA           Mn          Rec        192
## 1264      PConc       Gd       TA           Gd          GLQ       1258
## 1265     CBlock       TA       TA           No          Unf          0
## 1266     CBlock       TA       TA           No          BLQ        560
## 1270     CBlock       TA       Gd           No          BLQ        593
## 1271     CBlock       Gd       TA           Av          ALQ        528
## 1279     CBlock       TA       TA           No          ALQ        569
## 1280     CBlock       TA       TA           Av          Rec        812
## 1283      PConc       TA       TA           Av          GLQ       1014
## 1293      PConc       Gd       TA           No          GLQ        694
## 1294     CBlock       TA       TA           No          BLQ        547
## 1302     CBlock       Gd       TA           No          GLQ       1200
## 1305      PConc       Ex       TA           No          GLQ        482
## 1311      PConc       Gd       TA           Gd          Unf          0
## 1314     CBlock     <NA>     <NA>         <NA>         <NA>          0
## 1335      PConc       Gd       TA           No          Unf          0
## 1339     CBlock       TA       TA           No          BLQ        297
## 1341      PConc       Gd       TA           Gd          GLQ       1443
## 1347      PConc       Gd       TA           No          GLQ        735
## 1349     CBlock       TA       TA           No          Rec        340
## 1350     CBlock       TA       TA           No          GLQ        734
## 1351      PConc       Gd       TA           No          GLQ        378
## 1355     BrkTil       TA       Fa           No          Unf          0
## 1358      PConc       Gd       TA           No          GLQ        533
## 1361      PConc       Gd       TA           Av          GLQ        685
## 1366      PConc       Ex       TA           Gd          GLQ       1282
## 1373     CBlock       TA       TA           Mn          BLQ        865
## 1375     BrkTil       TA       TA           No          Unf          0
## 1388     CBlock       TA       TA           Av          BLQ        353
## 1399     CBlock       TA       TA           No          ALQ        625
## 1409      PConc       Gd       TA           Mn          GLQ        781
## 1411     CBlock       TA       TA           Av          Rec       1110
## 1415     CBlock       Gd       TA           Av          Unf          0
## 1416     CBlock       TA       TA           No          ALQ        457
## 1421     CBlock       Gd       TA           No          BLQ        678
## 1423     CBlock       Gd       TA           No          LwQ        958
## 1433      PConc       Gd       TA           Av          GLQ        697
## 1435     BrkTil       TA       TA           No          Unf          0
## 1438     CBlock       TA       TA           No          Rec        593
##      BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating HeatingQC CentralAir
## 8             BLQ         32       216        1107    GasA        Ex          Y
## 13            Unf          0       175         912    GasA        TA          Y
## 15            Unf          0       520        1253    GasA        TA          Y
## 17            Unf          0       426        1004    GasA        Ex          Y
## 25            ALQ        668       204        1060    GasA        Ex          Y
## 32            Unf          0      1228        1228    GasA        Gd          Y
## 43            LwQ         93         0         840    GasA        Gd          Y
## 44            BLQ        491       167         938    GasA        TA          Y
## 51            Unf          0       612         794    GasA        Gd          Y
## 65            Unf          0       318        1057    GasA        Ex          Y
## 67            Unf          0      1035        1947    GasA        TA          Y
## 77            Unf          0       724         952    GasA        Ex          Y
## 85            Unf          0       384         384    GasA        Gd          Y
## 96            Unf          0       370         680    GasA        Gd          Y
## 101           Unf          0       410        1610    GasA        Gd          Y
## 105           Unf          0       816        1040    GasA        Ex          Y
## 112           Unf          0       134         384    GasA        Ex          Y
## 114           Rec        869       905        1809    GasA        TA          Y
## 117           BLQ        670       252        1092    GasA        TA          Y
## 121           Unf          0         0         938    GasA        Ex          Y
## 127           Unf          0       958        1078    GasA        TA          Y
## 132           Unf          0       297         992    GasA        Ex          Y
## 134           Unf          0       262        1267    GasA        Ex          Y
## 137           Unf          0       519        1214    GasA        TA          Y
## 148           Unf          0       884         884    GasA        Ex          Y
## 150           Unf          0       896         896    GasA        Gd          Y
## 153           Unf          0       409         825    GasA        Gd          Y
## 154           ALQ       1080        93        1602    GasA        Gd          Y
## 161           Unf          0       572        1232    GasA        TA          Y
## 167           BLQ        768       470        1617    GasA        Ex          Y
## 170           Unf          0      1686        1686    GasA        TA          Y
## 171           Unf          0       360         720    GasA        TA          Y
## 178           BLQ        441       554        1052    GasA        Ex          Y
## 181           Unf          0       320         756    GasA        Ex          Y
## 187           Unf          0       577        1188    GasA        Ex          Y
## 192           Unf          0        99         725    GasA        Gd          Y
## 204           Unf          0       186         848    GasA        Ex          Y
## 208           Rec        306       375        1100    GasA        TA          Y
## 209           Unf          0        92        1157    GasA        Ex          Y
## 215           Unf          0       311         689    GasA        Ex          Y
## 219           Unf          0       457         798    GasA        Ex          Y
## 222           Unf          0      1010        1010    GasA        Ex          Y
## 237           Unf          0       594         996    GasA        Ex          Y
## 244           Unf          0       253         948    GasA        Ex          Y
## 249           Unf          0       747        1444    GasA        Gd          Y
## 269           Unf          0       392        1143    GasA        TA          Y
## 287           Unf          0       244         858    GasA        TA          Y
## 288           Unf          0       432         882    GasA        TA          Y
## 293           Unf          0         0         795    GasA        Fa          Y
## 307           Unf          0       319         319    GasA        TA          Y
## 308           Unf          0       599         861    GasA        Ex          Y
## 310           Unf          0       179         697    GasA        Gd          Y
## 319           Unf          0       336        1372    GasA        TA          Y
## 328           Unf          0       844         844    GasA        Gd          N
## 330           Rec        264      1424        1728    GasA        TA          Y
## 335           BLQ        147       103        1499    GasA        Ex          Y
## 342          <NA>          0         0           0    Wall        Fa          N
## 346           Unf          0       460         958    GasA        TA          Y
## 347           Unf          0       208        1478    GasA        Ex          Y
## 351           Unf          0       371        1453    GasA        Gd          Y
## 356           Unf          0       598        1158    GasA        Gd          Y
## 360           Unf          0       115         888    GasA        Ex          Y
## 361           Unf          0       484         883    GasA        Gd          Y
## 364           Unf          0        84         796    GasA        TA          Y
## 366           Unf          0       785        1394    GasA        Gd          Y
## 369           Rec        258       733        1063    GasA        Ex          Y
## 370           Unf          0       953         953    GasA        Ex          Y
## 375           Unf          0       333         683    GasA        Gd          N
## 384           Unf          0       595        1580    GasA        Ex          Y
## 392          <NA>          0         0           0    GasA        TA          Y
## 393           Unf          0       522         788    GasA        TA          Y
## 404           Unf          0       806         806    GasA        Gd          Y
## 405           Unf          0       165        1281    GasA        Ex          Y
## 412           Unf          0       892        1470    GasA        Ex          Y
## 421           Unf          0       356        1602    GasA        Gd          Y
## 426           Unf          0         0        1518    GasA        Gd          Y
## 447           Unf          0       930         930    GasA        Gd          Y
## 452           Unf          0       130         872    GasA        Ex          Y
## 457           Unf          0       248        1364    GasA        Ex          Y
## 458           Unf          0       588         588    GasA        Fa          Y
## 459           Unf          0       524         709    GasA        TA          Y
## 465           Unf          0      1375        1375    GasA        Ex          Y
## 470           BLQ       1120         0        1488    GasA        TA          Y
## 484           Unf          0       411         999    GasA        Gd          Y
## 490           Unf          0       264         264    GasA        TA          Y
## 496           Unf          0      1969        3200    GasA        Ex          Y
## 516           Unf          0       689         689    GasA        TA          Y
## 518           Unf          0        88         794    GasA        Ex          Y
## 536           Unf          0       264         864    GasA        TA          Y
## 537           Unf          0       378        1051    GasA        TA          Y
## 538           Unf          0       223        1581    GasA        Ex          Y
## 540           Unf          0       969         969    GasA        Ex          Y
## 544           LwQ        202         0        1204    GasA        Gd          Y
## 558           Unf          0      1374        1374    GasA        Ex          Y
## 559           Unf          0        90        1392    GasA        TA          Y
## 563           Unf          0       367        1095    GasA        Ex          Y
## 568           Unf          0         0         943    GasA        TA          Y
## 579           Rec        219       331        1144    GasA        Ex          Y
## 592           Unf          0       163         848    GasA        Ex          Y
## 609           Unf          0       536        1440    GasA        Ex          Y
## 610           Unf          0       427        1032    GasA        TA          Y
## 611           Unf          0       309        1299    GasA        Ex          Y
## 615           Unf          0       326         783    GasA        Ex          Y
## 622           Unf          0       336         756    GasA        Ex          Y
## 625           Unf          0       978         978    GasA        TA          Y
## 640           Unf          0       319        1057    GasA        Ex          Y
## 644           LwQ         35       664         981    GasA        TA          Y
## 658           Unf          0       793         793    GasA        TA          Y
## 664           Rec        723       111        1021    GasA        TA          Y
## 666           Unf          0       200        1188    GasA        Gd          Y
## 670           Unf          0       441        1208    GasA        TA          Y
## 677           Unf          0       161         988    GasA        TA          Y
## 680           Unf          0       288        1291    GasA        Ex          Y
## 683           LwQ        182       180        1190    GasA        Gd          Y
## 685           Unf          0       312         551    GasA        Ex          Y
## 688           Unf          0       151         848    GasA        Ex          Y
## 704           Unf          0       424        1643    GasA        TA          Y
## 707           Unf          0       876         876    GasA        TA          Y
## 712           Unf          0       192         725    GasA        Ex          Y
## 718           Unf          0       594        1742    GasA        TA          Y
## 719           Unf          0       186         848    GasA        Ex          Y
## 724           Unf          0        72         880    GasA        Ex          Y
## 732           Unf          0       948         948    GasA        TA          Y
## 743           ALQ         80       487        1142    GasA        Ex          Y
## 744           Unf          0       652         952    GasA        Ex          Y
## 749           Unf          0       660         660    GasA        Ex          Y
## 755           Unf          0       234         672    GasA        TA          Y
## 768           Unf          0       173         858    GasA        TA          Y
## 781           Unf          0         0        1097    GasA        Ex          Y
## 783           LwQ        630       491        1372    GasA        TA          Y
## 787           Unf          0       264         832    GasA        Gd          Y
## 789           Unf          0       490        1029    GasA        TA          Y
## 792           Unf          0       712         712    GasA        Ex          Y
## 809           Unf          0       186         848    GasA        Ex          Y
## 814           Unf          0       522        1008    GasA        Gd          Y
## 815           Unf          0       350        1568    GasA        Ex          Y
## 820           Unf          0       847         847    GasA        Ex          Y
## 826           ALQ        764         0        1013    GasA        TA          Y
## 838           Unf          0       516         672    GasA        TA          N
## 843           Unf          0         0        1390    GasA        TA          Y
## 849           Unf          0      1273        1273    GasA        Ex          Y
## 851           Unf          0       563        1127    GasA        TA          Y
## 853           Unf          0       381        1040    GasA        Ex          Y
## 854           LwQ        435         0         940    GasA        TA          Y
## 857           Unf          0       435        1054    GasA        TA          Y
## 863           Unf          0       174        1002    GasA        TA          Y
## 866          <NA>          0         0           0    GasA        Gd          Y
## 877           Unf          0       218         864    GasA        Ex          Y
## 880           Unf          0       808         808    GasA        Gd          Y
## 891           Unf          0       319        1383    GasA        TA          Y
## 898           Unf          0       536         858    GasA        TA          Y
## 902           Unf          0       277         876    GasA        TA          Y
## 906           ALQ        324       239         864    GasA        TA          Y
## 909           Unf          0      1072        1268    GasA        TA          Y
## 915           Unf          0        75        1134    GasA        Ex          Y
## 923           LwQ         93       266        1126    GasA        TA          Y
## 925           Unf          0       280         832    GasA        Gd          Y
## 926           Unf          0      1753        1753    GasA        Ex          Y
## 927           Unf          0       964         964    GasA        Gd          Y
## 936           Unf          0      1032        1032    GasA        Ex          Y
## 938           Unf          0       220         992    GasA        Ex          Y
## 941           Rec        354       354         819    GasA        Gd          Y
## 950           LwQ        193        29         784    GasA        Ex          Y
## 958           Unf          0       434        1330    GasA        TA          Y
## 964           Unf          0       196        1098    GasA        TA          Y
## 971           Unf          0        32         673    GasA        Ex          Y
## 975           Unf          0        77         944    GasA        Gd          Y
## 978           Unf          0      1128        1128    GasA        Ex          Y
## 983           Unf          0       692         848    GasA        TA          Y
## 991           Unf          0       135        1050    GasA        TA          Y
## 992           Unf          0      1442        1442    GasA        TA          Y
## 998           Unf          0      1680        1680    GasA        Fa          Y
## 1001          Unf          0       420        1657    GasA        Gd          Y
## 1012          Unf          0       184        1220    GasA        Gd          Y
## 1013          Unf          0       384         384    GasA        Gd          Y
## 1019          LwQ        400         0        1565    GasA        TA          Y
## 1025          Unf          0       686         686    GasA        Gd          Y
## 1027          Unf          0       326        1338    GasA        Ex          Y
## 1028          Unf          0       668        1654    GasA        Ex          Y
## 1030         <NA>          0         0           0    GasA        Ex          N
## 1032          Unf          0      1055        1055    GasA        Ex          Y
## 1036          Rec         64       336         800    GasA        Gd          Y
## 1040         <NA>          0         0           0    GasA        Ex          Y
## 1052          Unf          0       378         973    GasA        Ex          Y
## 1054          LwQ        557         0         854    GasA        TA          Y
## 1059          Rec        230       184        1154    GasA        Ex          Y
## 1072          Rec        791       230        1096    GasA        Ex          Y
## 1079          Unf          0        99         691    GasA        Gd          Y
## 1081          Unf          0       212         546    GasA        TA          Y
## 1092          Unf          0      1074        1074    GasA        Ex          Y
## 1103          Unf          0       924         924    GasA        Ex          Y
## 1105          Unf          0       554         773    GasA        Gd          Y
## 1111          Unf          0        55         408    GasA        Ex          Y
## 1117          Unf          0       672         672    GasA        Ex          Y
## 1119          Unf          0       384         384    GasA        Gd          Y
## 1133          Unf          0         0        1567    GasA        TA          Y
## 1136          Unf          0       399         780    GasA        Ex          Y
## 1138          Unf          0       718        1006    GasA        TA          Y
## 1141          Unf          0        40        1298    GasA        TA          Y
## 1143          Unf          0       572         572    GasA        TA          Y
## 1148          Unf          0       278         816    GasA        Ex          Y
## 1149          Unf          0       410         864    GasA        TA          Y
## 1156          Unf          0       568        1296    GasA        Ex          Y
## 1159          Rec        391       289        1360    GasA        Ex          Y
## 1172          Unf          0       350         818    GasA        TA          Y
## 1175          Unf          0         0        1216    GasA        Ex          Y
## 1185          Unf          0        90        1249    GasA        Ex          Y
## 1188          Unf          0       341        1224    GasA        Ex          Y
## 1201          Unf          0         0        1056    GasA        TA          Y
## 1208          Unf          0         0         648    GasA        Ex          Y
## 1225          Rec        211       652        1361    GasA        Ex          Y
## 1228          Unf          0       188        1188    GasA        Fa          Y
## 1238          Unf          0       792         792    GasA        Fa          Y
## 1241          Unf          0       473        1012    GasA        TA          Y
## 1245          Unf          0      1405        1405    GasA        Ex          Y
## 1247          ALQ       1031        36        1192    GasA        TA          Y
## 1254          Unf          0       841         841    GasA        Ex          Y
## 1256          Unf          0      1104        1104    GasA        Ex          Y
## 1262          Unf          0       536         728    GasA        Ex          Y
## 1264          Unf          0        74        1332    GasA        TA          Y
## 1265          Unf          0      1489        1489    GasA        Gd          Y
## 1266          LwQ        375         0         935    GasA        TA          Y
## 1270          Unf          0       130         723    GasA        TA          Y
## 1271          Unf          0      1152        1680    GasA        Fa          Y
## 1279          Rec         81       678        1328    GasA        TA          Y
## 1280          Unf          0       812        1624    GasA        Fa          Y
## 1283          Unf          0       138        1152    GasA        TA          Y
## 1293          Unf          0       284         978    GasA        Ex          Y
## 1294          Unf          0       224         771    GasA        Fa          Y
## 1302          Unf          0        78        1278    GasA        Gd          Y
## 1305          Unf          0       971        1453    GasA        Ex          Y
## 1311          Unf          0      1753        1753    GasA        Ex          Y
## 1314         <NA>          0         0           0   Floor        TA          N
## 1335          Unf          0      1284        1284    GasA        Ex          Y
## 1339          Rec         68      1203        1568    GasA        TA          Y
## 1341          Unf          0        39        1482    GasA        Ex          Y
## 1347          Unf          0       257         992    GasA        Ex          Y
## 1349          Unf          0       524         864    GasA        TA          Y
## 1350          Unf          0       344        1078    GasA        Ex          Y
## 1351          Unf          0       378         756    GasA        Ex          Y
## 1355          Unf          0       715         715    GasA        Gd          Y
## 1358          Unf          0       281         814    GasA        Ex          Y
## 1361          Unf          0       163         848    GasA        Ex          Y
## 1366          Unf          0      1351        2633    GasA        Ex          Y
## 1373          Unf          0       340        1205    GasA        Ex          Y
## 1375          Unf          0       816         816    GasA        Ex          Y
## 1388          Rec        334        60         747    GasA        TA          Y
## 1399          Unf          0       208         833    GasA        Ex          Y
## 1409          Unf          0       503        1284    GasA        Ex          Y
## 1411          Unf          0       734        1844    GasA        Gd          Y
## 1415          Unf          0       697         697    GasA        TA          Y
## 1416          Rec        374       193        1024    GasA        TA          Y
## 1421          Unf          0       762        1440    GasA        Ex          Y
## 1423          Unf          0         0         958    GasA        TA          Y
## 1433          Unf          0       151         848    GasA        Ex          Y
## 1435          Unf          0       952         952    Grav        Fa          N
## 1438          Unf          0       595        1188    GasA        TA          Y
##      Electrical X1stFlrSF X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## 8         SBrkr      1107       983            0      2090            1
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##      BsmtHalfBath FullBath HalfBath BedroomAbvGr KitchenAbvGr KitchenQual
## 8               0        2        1            3            1          TA
## 13              0        1        0            2            1          TA
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## 342             0        2        0            2            2          TA
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## 452             0        2        1            3            1          TA
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## 459             0        1        0            3            1          Gd
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## 484             0        1        0            3            1          Gd
## 490             0        1        1            3            1          TA
## 496             0        3        0            4            1          Gd
## 516             0        2        1            3            1          TA
## 518             0        2        1            3            1          TA
## 536             0        1        0            3            1          TA
## 537             0        1        1            3            1          TA
## 538             0        2        0            3            1          Gd
## 540             0        2        1            4            1          Gd
## 544             0        2        1            4            1          Gd
## 558             0        2        0            2            1          Gd
## 559             0        1        1            3            1          TA
## 563             0        2        1            4            1          Gd
## 568             0        1        0            2            1          TA
## 579             1        1        0            3            1          Gd
## 592             0        1        0            1            1          Gd
## 609             0        2        1            3            1          Ex
## 610             1        2        0            3            1          TA
## 611             0        2        1            3            1          Ex
## 615             0        2        1            3            1          Gd
## 622             0        2        1            2            1          Gd
## 625             0        1        0            3            1          TA
## 640             0        2        1            3            1          Gd
## 644             0        1        1            3            1          TA
## 658             0        2        1            3            1          TA
## 664             0        2        1            3            1          TA
## 666             0        1        1            3            1          TA
## 670             0        1        1            3            1          TA
## 677             0        1        0            3            1          TA
## 680             0        1        0            2            1          Gd
## 683             0        2        0            3            1          Gd
## 685             0        2        1            2            1          Gd
## 688             0        1        0            1            1          Gd
## 704             0        2        0            2            1          Gd
## 707             0        1        0            3            1          TA
## 712             0        2        1            3            1          Gd
## 718             0        2        0            2            1          Gd
## 719             0        1        0            1            1          Gd
## 724             0        2        0            3            1          Gd
## 732             0        1        0            3            1          TA
## 743             1        3        1            4            1          Gd
## 744             0        2        1            4            1          Gd
## 749             0        2        1            3            1          Gd
## 755             0        2        1            3            1          TA
## 768             0        1        0            2            1          TA
## 781             0        1        0            1            1          Gd
## 783             0        2        0            3            1          TA
## 787             0        2        1            5            1          Gd
## 789             0        1        0            3            1          TA
## 792             0        2        1            3            1          Gd
## 809             0        1        0            1            1          Gd
## 814             0        1        0            2            1          TA
## 815             0        2        0            3            1          Gd
## 820             0        2        1            3            1          Gd
## 826             1        2        1            3            1          TA
## 838             0        2        0            4            1          Fa
## 843             0        2        0            3            1          TA
## 849             0        2        0            2            1          Gd
## 851             0        1        1            3            1          TA
## 853             0        1        1            3            1          TA
## 854             0        1        0            3            1          TA
## 857             0        2        1            4            1          Gd
## 863             0        1        0            3            1          TA
## 866             0        2        0            2            1          TA
## 877             0        1        0            3            1          TA
## 880             0        2        1            3            1          TA
## 891             0        1        0            3            1          TA
## 898             0        1        0            2            1          TA
## 902             0        1        0            3            1          TA
## 906             0        1        0            2            1          TA
## 909             0        1        1            3            1          Gd
## 915             0        1        0            3            1          TA
## 923             1        2        0            3            1          Ex
## 925             0        2        1            4            1          TA
## 926             0        2        0            3            1          Ex
## 927             0        2        1            4            1          Gd
## 936             0        2        0            4            1          TA
## 938             0        2        1            3            1          Gd
## 941             1        1        0            3            1          Gd
## 950             1        2        1            4            1          TA
## 958             0        2        1            4            1          TA
## 964             0        1        0            3            1          TA
## 971             0        2        1            3            1          Gd
## 975             0        1        0            3            1          Ex
## 978             0        2        1            4            1          Gd
## 983             0        2        1            4            1          Gd
## 991             0        1        0            3            1          TA
## 992             0        2        0            2            1          TA
## 998             0        2        0            4            2          TA
## 1001            1        2        0            3            1          TA
## 1012            0        1        0            1            1          Gd
## 1013            0        2        1            3            1          Gd
## 1019            0        2        0            2            1          Gd
## 1025            0        2        0            5            2          TA
## 1027            0        2        1            5            1          Gd
## 1028            0        2        0            3            1          Gd
## 1030            0        1        0            3            1          TA
## 1032            0        2        1            3            1          Gd
## 1036            1        1        1            4            1          Gd
## 1040            0        2        0            4            1          TA
## 1052            0        2        1            3            1          Gd
## 1054            0        1        1            3            1          TA
## 1059            0        1        1            3            1          TA
## 1072            0        1        0            3            1          TA
## 1079            0        2        1            3            1          TA
## 1081            0        1        1            3            1          TA
## 1092            0        1        1            2            1          Gd
## 1103            0        2        1            3            1          TA
## 1105            0        2        1            3            1          TA
## 1111            0        2        1            3            1          Gd
## 1117            0        1        0            3            1          TA
## 1119            0        2        1            3            1          Gd
## 1133            0        2        0            2            1          Gd
## 1136            0        2        1            4            1          Gd
## 1138            0        1        0            3            1          TA
## 1141            0        2        0            3            1          Gd
## 1143            0        1        0            2            1          TA
## 1148            0        1        0            2            1          TA
## 1149            0        1        2            4            1          Gd
## 1156            0        1        0            3            1          Gd
## 1159            0        1        1            2            1          Gd
## 1172            0        1        0            3            1          TA
## 1175            0        2        1            4            1          TA
## 1185            0        1        0            3            1          TA
## 1188            0        2        0            2            1          TA
## 1201            0        1        0            2            1          TA
## 1208            1        0        0            0            1          TA
## 1225            0        2        2            4            2          TA
## 1228            0        1        0            3            1          TA
## 1238            0        1        0            3            1          Gd
## 1241            0        1        0            3            1          TA
## 1245            0        2        0            2            1          Gd
## 1247            0        2        1            3            1          Gd
## 1254            0        2        1            3            1          TA
## 1256            0        1        0            5            1          TA
## 1262            0        3        1            4            1          Gd
## 1264            0        0        1            0            1          Gd
## 1265            0        2        0            3            1          Gd
## 1266            0        1        0            3            1          TA
## 1270            1        1        1            3            1          TA
## 1271            0        1        1            3            1          TA
## 1279            0        1        1            3            1          TA
## 1280            1        2        0            4            1          TA
## 1283            0        1        0            3            1          TA
## 1293            0        2        1            3            1          Gd
## 1294            0        1        0            3            1          Gd
## 1302            0        2        0            3            1          Gd
## 1305            0        2        1            4            1          Gd
## 1311            0        2        0            3            1          Gd
## 1314            0        1        0            2            1          TA
## 1335            0        2        1            3            1          Gd
## 1339            0        2        0            3            1          TA
## 1341            0        2        0            3            1          Gd
## 1347            0        2        1            3            1          Gd
## 1349            0        1        0            3            1          TA
## 1350            0        1        1            3            1          TA
## 1351            0        2        1            3            1          Gd
## 1355            0        2        0            4            1          TA
## 1358            0        2        1            3            1          Gd
## 1361            0        1        0            1            1          Gd
## 1366            0        2        1            2            1          Ex
## 1373            0        2        1            4            1          TA
## 1375            0        2        0            3            1          Gd
## 1388            0        1        0            3            1          TA
## 1399            0        1        0            3            1          TA
## 1409            0        2        1            3            1          Gd
## 1411            0        2        0            3            1          Gd
## 1415            0        2        0            4            1          Gd
## 1416            0        1        0            2            1          TA
## 1421            0        2        0            3            1          Gd
## 1423            0        2        0            2            1          TA
## 1433            0        1        0            1            1          Gd
## 1435            0        1        0            2            1          Fa
## 1438            0        1        0            3            1          TA
##      TotRmsAbvGrd Functional Fireplaces FireplaceQu GarageType GarageYrBlt
## 8               7        Typ          2          TA     Attchd        1973
## 13              4        Typ          0        <NA>     Detchd        1962
## 15              5        Typ          1          Fa     Attchd        1960
## 17              5        Typ          1          TA     Attchd        1970
## 25              6        Typ          1          TA     Attchd        1968
## 32              6        Typ          0        <NA>     Attchd        1966
## 43              5        Typ          0        <NA>     Attchd        1983
## 44              5        Typ          0        <NA>     Detchd        1977
## 51              6        Typ          0        <NA>     Attchd        1997
## 65              8        Typ          0        <NA>     Attchd        1998
## 67              7       Min1          1          Gd     Attchd        1970
## 77              4        Typ          0        <NA>     Detchd        1956
## 85              7        Typ          1          TA    BuiltIn        1995
## 96              6        Typ          1          TA    BuiltIn        1993
## 101             6        Typ          2          TA     Attchd        1977
## 105             7        Typ          2          TA     Detchd        1951
## 112             7        Typ          1          TA    BuiltIn        2000
## 114             7        Typ          2          Gd    Basment        1953
## 117             6        Typ          1          Po     Attchd        1962
## 121             4        Typ          2          TA     Attchd        1969
## 127             5        Typ          1          TA     Attchd        1977
## 132             7        Typ          1          TA    BuiltIn        2000
## 134             6        Typ          0        <NA>     Attchd        2001
## 137             5        Typ          1          Fa     Attchd        1967
## 148             8        Typ          1          Gd    BuiltIn        2001
## 150             7        Typ          0        <NA>     Detchd        1936
## 153             8        Typ          1          Gd     Attchd        1971
## 154             4        Typ          1          Gd     Attchd        1960
## 161             6        Typ          0        <NA>     Attchd        1984
## 167             7        Typ          3          Gd     Attchd        1955
## 170             6        Typ          1          TA     Attchd        1981
## 171             7        Typ          0        <NA>     Detchd        1991
## 178             8        Typ          1          Gd     Attchd        1958
## 181             5        Typ          1          TA     Detchd        2000
## 187             6        Typ          0        <NA>     Attchd        1990
## 192             7        Typ          0        <NA>     Attchd        1972
## 204             3        Typ          1          Gd     Attchd        2004
## 208             6        Typ          1          Po     Attchd        1960
## 209             7        Typ          1          Gd     Attchd        1988
## 215             6        Typ          0        <NA>     Attchd        1977
## 219             8        Typ          2          TA     Attchd        1939
## 222             8        Typ          1          TA    BuiltIn        2002
## 237             7        Typ          0        <NA>     Attchd        1993
## 244             8        Typ          2          Fa     Attchd        1994
## 249             7        Typ          2          TA     Attchd        1958
## 269             6        Typ          1          Fa     Attchd        1987
## 287             5        Typ          0        <NA>       <NA>           0
## 288             5        Typ          0        <NA>     Detchd        1970
## 293             9        Typ          1          TA     Attchd        1977
## 307             6        Typ          0        <NA>       <NA>           0
## 308             4        Typ          0        <NA>     Detchd        1961
## 310             6        Typ          1          TA     Attchd        1993
## 319             6        Typ          2          TA     Attchd        1980
## 328            10        Typ          0        <NA>     Detchd        1930
## 330            10        Typ          0        <NA>     Detchd        2002
## 335             7        Typ          2          Gd     Attchd        1965
## 342             6        Typ          0        <NA>     Detchd        1949
## 346             5        Typ          0        <NA>     Attchd        1960
## 347             6        Typ          2          Gd     Attchd        1960
## 351             6        Typ          1          TA     Attchd        1986
## 356             6        Typ          0        <NA>     Attchd        1992
## 360             5        Typ          1          TA     Attchd        1978
## 361             8        Typ          0        <NA>     Detchd        1940
## 364             6        Typ          1          TA     Attchd        1976
## 366             6        Typ          2          Gd     Attchd        1963
## 369             7        Typ          1          Gd     Detchd        1997
## 370             7        Typ          1          TA     Attchd        2000
## 375             4       Maj1          0        <NA>       <NA>           0
## 384             9        Typ          2          Fa     Attchd        1992
## 392             5        Typ          0        <NA>     Attchd        1959
## 393             4        Typ          2          TA       <NA>           0
## 404             7        Typ          1          TA    BuiltIn        1995
## 405             8       Min1          1          TA     Attchd        1993
## 412             7        Typ          1          Gd     Attchd        2009
## 421             8        Typ          1          TA     Attchd        1977
## 426             5        Typ          1          TA     Attchd        1989
## 447            10        Typ          1          TA     Attchd        1998
## 452             7        Typ          0        <NA>     Attchd        1996
## 457             6       Min1          2          Gd     Attchd        1954
## 458             7        Typ          1          Gd     Detchd        1925
## 459             5        Typ          1          TA     Detchd        1950
## 465             6        Typ          1          TA     Attchd        2004
## 470             4        Typ          0        <NA>     Attchd        1985
## 484             6        Typ          0        <NA>     Detchd        1963
## 490             4        Typ          1          Gd    BuiltIn        1976
## 496            10        Typ          1          Gd     Attchd        1992
## 516             7        Typ          1          TA     Attchd        1972
## 518             7        Typ          0        <NA>     Attchd        1998
## 536             5        Typ          0        <NA>     Detchd        1980
## 537             7        Typ          1          Fa     Attchd        1968
## 538             6        Typ          1          TA     Attchd        2001
## 540             8        Typ          1          TA    BuiltIn        2000
## 544             9        Typ          0        <NA>     Attchd        1988
## 558             7        Typ          1          TA     Attchd        2003
## 559             5        Mod          1          Gd     Detchd        1957
## 563             9        Typ          1          TA     Attchd        1992
## 568             4        Typ          2          TA     Detchd        1979
## 579             7        Typ          2          Gd     Attchd        1960
## 592             4        Typ          0        <NA>     Attchd        2003
## 609             8        Typ          2          Ex     Attchd        2000
## 610             6        Typ          1          TA     Attchd        1978
## 611             7        Typ          1          TA    BuiltIn        2001
## 615             7        Typ          1          Gd     Attchd        2002
## 622             4        Typ          1          TA     Detchd        2000
## 625             6       Min1          1          TA     Attchd        1960
## 640             7        Typ          1          TA     Attchd        2001
## 644             5        Typ          0        <NA>     Detchd        1979
## 658             7        Typ          1          TA     Attchd        1976
## 664             7        Sev          1          Po    CarPort        1965
## 666             6       Min2          1          TA     Detchd        1999
## 670             6        Typ          1          TA     Attchd        1977
## 677             5        Typ          0        <NA>     Detchd        1963
## 680             6        Typ          1          Gd     Attchd        1996
## 683             6       Min1          1          TA     Attchd        1984
## 685             4        Typ          0        <NA>     Detchd        2004
## 688             3        Typ          1          TA     Attchd        2004
## 704             5        Typ          2          TA     Attchd        1971
## 707             6        Typ          0        <NA>     Attchd        1966
## 712             6        Typ          0        <NA>     Attchd        1976
## 718             5        Typ          1          TA     Attchd        1985
## 719             3        Typ          0        <NA>     Attchd        2004
## 724             5        Typ          1          Gd     Attchd        1988
## 732             6        Typ          0        <NA>     Attchd        1968
## 743            11        Typ          2          TA    BuiltIn        1994
## 744             8        Typ          1          TA    BuiltIn        2000
## 749             6        Typ          0        <NA>     Attchd        2003
## 755             6        Typ          1          TA     Attchd        1978
## 768             5        Typ          0        <NA>     Detchd        1983
## 781             4        Typ          1          TA     Attchd        1978
## 783             7        Typ          1          Gd     Attchd        1967
## 787             9        Typ          0        <NA>     Attchd        1966
## 789             5        Typ          2          TA     Attchd        1976
## 792             7        Typ          1          TA     Attchd        1994
## 809             4        Typ          1          Gd     Attchd        2004
## 814             4        Typ          1          Gd     Attchd        1954
## 815             7        Typ          2          Gd     Attchd        2002
## 820             7        Typ          1          Gd    BuiltIn        2003
## 826             7       Min2          0        <NA>     Attchd        1967
## 838             7        Typ          0        <NA>     Detchd        1934
## 843             6       Min2          2          TA    Basment        1975
## 849             7        Typ          1          TA     Attchd        2003
## 851             7        Typ          1          Fa     Attchd        1964
## 853             6        Typ          0        <NA>     Detchd        1962
## 854             5        Typ          0        <NA>     Detchd        1981
## 857             9        Typ          1          Gd     Attchd        1968
## 863             5        Typ          0        <NA>     Detchd        1973
## 866             7        Typ          1          TA     Attchd        1979
## 877             6        Typ          0        <NA>     Attchd        1978
## 880             7        Typ          1          TA    BuiltIn        1993
## 891             6        Typ          1          Gd     Attchd        1954
## 898             4        Typ          0        <NA>     Detchd        1979
## 902             6        Typ          0        <NA>     Attchd        1967
## 906             5        Typ          0        <NA>     Attchd        1983
## 909             5        Typ          0        <NA>     Detchd        1978
## 915             6        Typ          0        <NA>     Attchd        1956
## 923             6        Typ          0        <NA>     Attchd        1977
## 925             9        Typ          1          Gd     Attchd        1968
## 926             7        Typ          1          TA     Attchd        2001
## 927             8        Typ          1          TA    BuiltIn        1997
## 936            10        Typ          2          TA     Attchd        1940
## 938             8        Typ          1          TA    BuiltIn        1999
## 941             7        Typ          1          Gd    Basment        1958
## 950             7       Min2          1          Po     Attchd        1969
## 958            11        Typ          1          TA     Attchd        1977
## 964             6        Typ          0        <NA>     Attchd        1955
## 971             6        Typ          0        <NA>     Detchd        2000
## 975             6        Typ          0        <NA>     Attchd        1961
## 978             9        Typ          1          Gd     Attchd        2002
## 983             8        Typ          1          TA     Attchd        1976
## 991             6        Typ          0        <NA>     Attchd        1961
## 992             6        Typ          1          TA     Attchd        1970
## 998             8        Typ          0        <NA>     Detchd        1976
## 1001            7        Typ          1          TA     Attchd        1970
## 1012            4        Typ          1          Ex     Attchd        1984
## 1013            7        Typ          1          TA     Attchd        1991
## 1019           10        Typ          1          Gd     Attchd        1976
## 1025           10        Typ          0        <NA>       <NA>           0
## 1027           10        Typ          1          TA     Attchd        1993
## 1028            6        Typ          0        <NA>     Attchd        2002
## 1030            5        Typ          0        <NA>     Detchd        1957
## 1032            7        Typ          1          TA    BuiltIn        2001
## 1036            7        Typ          0        <NA>     Attchd        1966
## 1040            8       Min2          1          Gd     Attchd        1955
## 1052            7        Typ          1          Gd    BuiltIn        1994
## 1054            7        Typ          1          Gd     Detchd        1977
## 1059            6        Typ          1          Po     Attchd        1966
## 1072            6        Typ          0        <NA>     Attchd        1969
## 1079            6        Typ          1          TA     Attchd        1995
## 1081            6        Typ          0        <NA>     Attchd        1973
## 1092            5        Typ          0        <NA>     Attchd        1987
## 1103            7        Typ          1          TA     Attchd        2000
## 1105            8        Typ          1          TA     Attchd        1995
## 1111            7        Typ          1          TA    BuiltIn        2002
## 1117            5        Typ          0        <NA>    Basment        1956
## 1119            7        Typ          1          TA     Attchd        1992
## 1133            5        Typ          2          TA     Attchd        1977
## 1136            9        Typ          1          TA     Attchd        1976
## 1138            5        Typ          0        <NA>       <NA>           0
## 1141            5        Typ          1          TA     Attchd        1985
## 1143            5        Typ          1          Gd     Detchd        1982
## 1148            5        Typ          0        <NA>     Detchd        2002
## 1149            8        Typ          2          Gd     Attchd        1965
## 1156            7        Typ          1          Gd     Detchd        1993
## 1159            5        Typ          1          TA     Attchd        1978
## 1172            5        Typ          0        <NA>     Detchd        1926
## 1175            8        Typ          0        <NA>     Attchd        1990
## 1185            7        Typ          1          TA     2Types        1975
## 1188            5        Typ          0        <NA>     Attchd        1999
## 1201            5        Typ          0        <NA>     Detchd        1966
## 1208            3        Typ          0        <NA>     Attchd        1965
## 1225           12        Typ          1          TA    BuiltIn        1977
## 1228            6        Typ          0        <NA>     Attchd        1959
## 1238            7        Typ          2          Gd     Detchd        1931
## 1241            6        Typ          0        <NA>     Attchd        1976
## 1245            6        Typ          1          Gd     Attchd        2003
## 1247            9        Typ          2          Gd     Attchd        1974
## 1254            7        Typ          1          TA    BuiltIn        1999
## 1256            8       Min2          2          TA     Attchd        1957
## 1262           11        Typ          2          Gd    BuiltIn        1982
## 1264            4        Typ          1          TA     Attchd        1979
## 1265            7        Typ          1          Gd     Attchd        1968
## 1266            5        Typ          0        <NA>     Attchd        1965
## 1270            6        Typ          1          TA     Attchd        1972
## 1271            7        Typ          1          Gd     Attchd        1967
## 1279            6        Typ          2          Gd     Attchd        1963
## 1280            7        Typ          0        <NA>     Attchd        1964
## 1283            6        Typ          1          Gd     Attchd        1964
## 1293            9        Typ          1          TA     Attchd        1999
## 1294            7        Typ          2          Gd     Attchd        1942
## 1302            6        Typ          0        <NA>     Attchd        1991
## 1305            9        Typ          1          Ex     Attchd        1990
## 1311            7        Typ          1          TA     Attchd        2001
## 1314            4        Typ          0        <NA>     Detchd        1955
## 1335            7        Typ          1          Gd     Attchd        2002
## 1339            9        Typ          1          Gd     Attchd        1968
## 1341            5        Typ          1          Fa     Attchd        1998
## 1347            7        Typ          1          TA     Attchd        2000
## 1349            5        Typ          0        <NA>     Attchd        1966
## 1350            6        Typ          1          Fa     Attchd        1971
## 1351            5        Typ          0        <NA>     Detchd        2000
## 1355            7        Typ          1          Gd     Attchd        1920
## 1358            7        Typ          0        <NA>     Attchd        2000
## 1361            4        Typ          0        <NA>     Attchd        2003
## 1366            8        Typ          2          Gd     Attchd        2001
## 1373            7        Typ          2          Gd     Attchd        1970
## 1375            7        Typ          0        <NA>     Attchd        2007
## 1388            7       Min1          2          TA     Detchd        1966
## 1399            5        Typ          0        <NA>       <NA>           0
## 1409            7        Typ          1          TA     Attchd        1998
## 1411            7        Typ          1          TA     Attchd        1969
## 1415            8        Typ          1          Gd     Attchd        1966
## 1416            6       Min1          1          TA     Detchd        1970
## 1421            7        Typ          1          TA     Attchd        1981
## 1423            5        Typ          0        <NA>     Attchd        1976
## 1433            3        Typ          1          TA     Attchd        2004
## 1435            4        Typ          1          Gd     Detchd        1916
## 1438            6        Typ          0        <NA>     Attchd        1962
##      GarageFinish GarageCars GarageArea GarageQual GarageCond PavedDrive
## 8             RFn          2        484         TA         TA          Y
## 13            Unf          1        352         TA         TA          Y
## 15            RFn          1        352         TA         TA          Y
## 17            Fin          2        480         TA         TA          Y
## 25            Unf          1        270         TA         TA          Y
## 32            Unf          1        271         TA         TA          Y
## 43            RFn          2        504         TA         Gd          Y
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## 1293          0           0             0          0           0        0
## 1294          0           0             0          0         224        0
## 1302        112          51             0          0           0        0
## 1305        500           0             0          0           0        0
## 1311        198         150             0          0           0        0
## 1314          0           0             0          0           0        0
## 1335        192          87             0          0           0        0
## 1339          0          80             0        290           0        0
## 1341        402          25             0          0           0        0
## 1347          0         184             0          0           0        0
## 1349          0           0             0          0           0        0
## 1350          0           0             0          0           0        0
## 1351          0          32             0          0           0        0
## 1355         55           0             0          0           0        0
## 1358          0          96             0          0           0        0
## 1361        140           0             0          0           0        0
## 1366        314         140             0          0           0        0
## 1373          0          42             0          0           0        0
## 1375          0           0           112          0           0        0
## 1388          0           0            50          0           0        0
## 1399          0           0             0          0           0        0
## 1409          0         126             0          0           0        0
## 1411          0          73           216          0           0        0
## 1415        586         236             0          0           0      738
## 1416        316          28             0          0           0        0
## 1421          0           0            99          0           0        0
## 1423          0          60             0          0           0        0
## 1433        149           0             0          0           0        0
## 1435          0          98             0          0          40        0
## 1438        261          39             0          0           0        0
##      PoolQC Fence MiscFeature MiscVal MoSold YrSold SaleType SaleCondition
## 8      <NA>  <NA>        Shed     350     11   2009       WD        Normal
## 13     <NA>  <NA>        <NA>       0      9   2008       WD        Normal
## 15     <NA>  GdWo        <NA>       0      5   2008       WD        Normal
## 17     <NA>  <NA>        Shed     700      3   2010       WD        Normal
## 25     <NA> MnPrv        <NA>       0      5   2010       WD        Normal
## 32     <NA> MnPrv        <NA>       0      6   2008       WD        Normal
## 43     <NA> MnPrv        <NA>       0     12   2007       WD        Normal
## 44     <NA> MnPrv        <NA>       0      7   2008       WD        Normal
## 51     <NA>  <NA>        <NA>       0      7   2007       WD        Normal
## 65     <NA> GdPrv        <NA>       0      2   2009       WD        Normal
## 67     <NA>  <NA>        <NA>       0      7   2010       WD        Normal
## 77     <NA>  <NA>        <NA>       0      4   2008       WD        Normal
## 85     <NA>  <NA>        Shed     700      5   2009       WD        Normal
## 96     <NA>  <NA>        Shed     480      4   2009       WD        Normal
## 101    <NA>  <NA>        <NA>       0      2   2010       WD        Normal
## 105    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 112    <NA>  <NA>        <NA>       0      4   2010       WD        Normal
## 114    <NA> MnPrv        <NA>       0     10   2007      COD       Abnorml
## 117    <NA>  <NA>        <NA>       0      9   2009       WD        Normal
## 121    <NA>  <NA>        <NA>       0     10   2006       WD        Normal
## 127    <NA>  <NA>        <NA>       0      2   2007       WD        Normal
## 132    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 134    <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 137    <NA>  <NA>        <NA>       0      7   2007       WD        Normal
## 148    <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 150    <NA>  <NA>        <NA>       0      4   2006       WD        Normal
## 153    <NA>  GdWo        <NA>       0      6   2006       WD        Normal
## 154    <NA>  <NA>        <NA>       0      3   2008       WD        Normal
## 161    <NA>  <NA>        <NA>       0      6   2008       WD        Normal
## 167    <NA>  GdWo        <NA>       0     11   2009      COD        Normal
## 170    <NA>  <NA>        <NA>       0      1   2006       WD        Normal
## 171    <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 178    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 181    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 187    <NA> GdPrv        <NA>       0      6   2009       WD        Normal
## 192    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 204    <NA>  <NA>        <NA>       0      1   2008       WD        Normal
## 208    <NA>  GdWo        <NA>       0      4   2008       WD        Normal
## 209    <NA>  <NA>        <NA>       0      4   2007       WD        Normal
## 215    <NA> MnPrv        Shed     450      3   2010       WD        Normal
## 219    <NA>  <NA>        <NA>       0      5   2008       WD        Normal
## 222    <NA>  <NA>        <NA>       0     12   2009    ConLI        Normal
## 237    <NA>  <NA>        <NA>       0      2   2010       WD        Normal
## 244    <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 249    <NA>  <NA>        Shed     500      6   2007       WD        Normal
## 269    <NA> GdPrv        <NA>       0      5   2007       WD        Normal
## 287    <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 288    <NA> MnPrv        <NA>       0      2   2010       WD        Normal
## 293    <NA>  <NA>        <NA>       0      3   2006       WD        Normal
## 307    <NA> MnPrv        <NA>       0      3   2008       WD        Normal
## 308    <NA>  <NA>        <NA>       0      3   2009       WD        Normal
## 310    <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 319    <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 328    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 330    <NA>  <NA>        <NA>       0     11   2007       WD        Normal
## 335    <NA>  <NA>        Shed     700      8   2008       WD        Normal
## 342    <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 346    <NA>  <NA>        Gar2   15500      4   2007       WD        Normal
## 347    <NA>  <NA>        <NA>       0     12   2009       WD        Normal
## 351    <NA>  <NA>        <NA>       0     12   2006       WD       Abnorml
## 356    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 360    <NA> MnPrv        <NA>       0      6   2007       WD        Normal
## 361    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 364    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 366    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 369    <NA>  <NA>        <NA>       0      3   2010       WD        Normal
## 370    <NA>  <NA>        <NA>       0      1   2006       WD        Normal
## 375    <NA>  <NA>        <NA>       0      3   2009       WD        Normal
## 384    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 392    <NA> MnPrv        Shed    1200      7   2007       WD        Normal
## 393    <NA>  GdWo        <NA>       0      4   2006       WD       Abnorml
## 404    <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 405    <NA>  GdWo        <NA>       0      6   2009       WD        Normal
## 412    <NA>  <NA>        <NA>       0      6   2010      New       Partial
## 421    <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 426    <NA>  <NA>        <NA>       0      8   2009       WD        Normal
## 447    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 452    <NA>  <NA>        <NA>       0      7   2007       WD        Normal
## 457    <NA>  <NA>        <NA>       0      3   2008       WD        Normal
## 458    <NA> MnPrv        <NA>       0      6   2008       WD        Normal
## 459    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 465    <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 470    <NA>  <NA>        <NA>       0      6   2010       WD        Normal
## 484    <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 490    <NA>  <NA>        <NA>       0      6   2008       WD        Normal
## 496    <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 516    <NA> GdPrv        <NA>       0      8   2009      COD       Abnorml
## 518    <NA> MnPrv        <NA>       0      5   2007       WD        Normal
## 536    <NA>  MnWw        <NA>       0      4   2008      COD        Normal
## 537    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 538    <NA> MnPrv        Shed    2000      5   2010       WD        Normal
## 540    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 544    <NA>  <NA>        <NA>       0      2   2006       WD        Normal
## 558    <NA>  <NA>        <NA>       0     10   2006       WD        Normal
## 559    <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 563    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 568    <NA>  <NA>        <NA>       0     12   2006       WD        Normal
## 579    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 592    <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 609    <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 610    <NA> MnPrv        Shed     500      7   2007       WD        Normal
## 611    <NA>  <NA>        <NA>       0     11   2009       WD        Normal
## 615    <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 622    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 625    <NA>  GdWo        Shed     600      8   2007       WD        Normal
## 640    <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 644    <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 658    <NA>  <NA>        <NA>       0     11   2007       WD        Normal
## 664    <NA>  <NA>        <NA>       0      8   2007       WD       Abnorml
## 666    <NA>  <NA>        <NA>       0     11   2006       WD        Normal
## 670    <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 677    <NA>  <NA>        <NA>       0     10   2007       WD        Normal
## 680    <NA>  <NA>        <NA>       0     11   2008       WD        Normal
## 683    <NA>  <NA>        <NA>       0      9   2007       WD        Normal
## 685    <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 688    <NA>  <NA>        <NA>       0      5   2008       WD        Normal
## 704    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 707    <NA> MnPrv        <NA>       0     12   2008       WD       Abnorml
## 712    <NA>  <NA>        <NA>       0      3   2010       WD        Normal
## 718    <NA>  <NA>        <NA>       0     12   2006       WD        Normal
## 719    <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 724    <NA>  <NA>        <NA>       0     12   2009       WD        Normal
## 732    <NA>  <NA>        <NA>       0      5   2007       WD        Family
## 743    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 744    <NA>  <NA>        <NA>       0      4   2009       WD        Normal
## 749    <NA>  <NA>        <NA>       0      8   2007       WD        Normal
## 755    <NA> GdPrv        <NA>       0      4   2010       WD       Abnorml
## 768    <NA>  <NA>        <NA>       0      4   2009       WD        Normal
## 781    <NA>  <NA>        <NA>       0      7   2009       WD        Normal
## 783    <NA>  <NA>        <NA>       0      9   2009       WD        Normal
## 787    <NA>  <NA>        <NA>       0      7   2007       WD        Normal
## 789    <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 792    <NA>  <NA>        Shed     500     10   2008       WD        Normal
## 809    <NA>  <NA>        <NA>       0      6   2008    ConLD        Normal
## 814    <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 815    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 820    <NA>  <NA>        <NA>       0     10   2007       WD        Family
## 826    <NA>  <NA>        <NA>       0      6   2009       WD       Abnorml
## 838    <NA>  <NA>        <NA>       0      3   2008       WD        Normal
## 843    <NA>  <NA>        <NA>       0      1   2007       WD        Normal
## 849    <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 851    <NA> MnPrv        <NA>       0      8   2009       WD        Normal
## 853    <NA>  <NA>        <NA>       0      4   2010       WD        Normal
## 854    <NA> MnPrv        <NA>       0     10   2008       WD        Normal
## 857    <NA>  <NA>        <NA>       0      8   2006       WD        Normal
## 863    <NA> MnPrv        <NA>       0      8   2009       WD        Normal
## 866    <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 877    <NA>  GdWo        <NA>       0      7   2009       WD        Normal
## 880    <NA> MnPrv        <NA>       0     12   2009       WD        Normal
## 891    <NA> GdPrv        <NA>       0      6   2008       WD        Normal
## 898    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 902    <NA> MnPrv        <NA>       0      8   2007       WD        Normal
## 906    <NA> MnPrv        <NA>       0      6   2006       WD        Normal
## 909    <NA>  <NA>        <NA>       0     10   2009       WD        Normal
## 915    <NA>  <NA>        <NA>       0      4   2009       WD        Normal
## 923    <NA>  <NA>        <NA>       0      3   2008       WD       Abnorml
## 925    <NA> GdPrv        <NA>       0      4   2008       WD        Normal
## 926    <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 927    <NA>  <NA>        <NA>       0     11   2006       WD        Normal
## 936    <NA>  <NA>        <NA>       0      6   2010      COD        Normal
## 938    <NA> GdPrv        <NA>       0      6   2009       WD        Normal
## 941    <NA>  <NA>        <NA>       0      1   2009      COD       Abnorml
## 950    <NA> MnPrv        Shed     400      9   2008       WD        Normal
## 958    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 964    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 971    <NA>  <NA>        <NA>       0      4   2006       WD        Normal
## 975    <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 978    <NA>  <NA>        <NA>       0      5   2008       WD        Normal
## 983    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 991    <NA>  <NA>        <NA>       0      1   2006      COD        Normal
## 992    <NA>  <NA>        <NA>       0      2   2009       WD        Normal
## 998    <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 1001   <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 1012   <NA>  <NA>        <NA>       0      8   2009      COD       Abnorml
## 1013   <NA>  <NA>        <NA>       0      5   2007       WD        Normal
## 1019   <NA>  <NA>        <NA>       0      5   2008      COD       Abnorml
## 1025   <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 1027   <NA>  <NA>        <NA>       0     11   2006       WD       Abnorml
## 1028   <NA>  <NA>        <NA>       0      2   2006       WD        Normal
## 1030   <NA>  <NA>        <NA>       0      1   2009       WD        Normal
## 1032   <NA>  <NA>        <NA>       0      9   2008       WD        Normal
## 1036   <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 1040   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1052   <NA>  <NA>        <NA>       0      1   2009       WD        Normal
## 1054   <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 1059   <NA> MnPrv        <NA>       0     11   2009       WD        Normal
## 1072   <NA>  <NA>        <NA>       0      3   2006       WD       Abnorml
## 1079   <NA>  <NA>        <NA>       0      7   2006       WD        Normal
## 1081   <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 1092   <NA>  <NA>        <NA>       0     10   2007       WD        Normal
## 1103   <NA>  <NA>        <NA>       0     11   2007       WD       Abnorml
## 1105   <NA>  <NA>        <NA>       0      6   2008       WD        Normal
## 1111   <NA>  <NA>        <NA>       0      3   2009       WD        Normal
## 1117   <NA> MnPrv        <NA>       0     10   2009      COD       Abnorml
## 1119   <NA>  <NA>        <NA>       0      7   2007       WD        Normal
## 1133   <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 1136   <NA>  <NA>        <NA>       0     10   2009       WD        Normal
## 1138   <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 1141   <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 1143   <NA>  <NA>        <NA>       0      8   2008       WD        Normal
## 1148   <NA>  <NA>        <NA>       0      6   2008       WD        Normal
## 1149   <NA> GdPrv        <NA>       0      5   2008       WD        Normal
## 1156   <NA>  <NA>        <NA>       0     11   2008       WD        Normal
## 1159   <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 1172   <NA>  <NA>        <NA>       0     12   2009       WD        Normal
## 1175   <NA> GdPrv        <NA>       0      4   2006       WD        Normal
## 1185   <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 1188   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1201   <NA> MnPrv        <NA>       0     11   2006       WD        Normal
## 1208   <NA>  <NA>        <NA>       0      5   2006       WD        Normal
## 1225   <NA>  <NA>        Gar2    8300      8   2007       WD        Normal
## 1228   <NA> MnPrv        <NA>       0      5   2010      COD       Abnorml
## 1238   <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 1241   <NA>  <NA>        <NA>       0      5   2010       WD        Normal
## 1245   <NA>  <NA>        <NA>       0      3   2006       WD        Normal
## 1247   <NA> MnPrv        <NA>       0      7   2007       WD        Normal
## 1254   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1256   <NA>  <NA>        <NA>       0     11   2009       WD        Normal
## 1262   <NA>  GdWo        <NA>       0      5   2008       WD        Normal
## 1264   <NA>  <NA>        <NA>       0      4   2010       WD        Normal
## 1265   <NA>  <NA>        <NA>       0      8   2009       WD        Normal
## 1266   <NA> MnPrv        <NA>       0     11   2006       WD        Normal
## 1270   <NA>  <NA>        <NA>       0     12   2009       WD        Normal
## 1271   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1279   <NA>  <NA>        <NA>       0      6   2010       WD        Normal
## 1280   <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 1283   <NA>  <NA>        <NA>       0      4   2010       WD        Normal
## 1293   <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 1294   <NA>  <NA>        <NA>       0     11   2009       WD        Normal
## 1302   <NA>  GdWo        <NA>       0      6   2008       WD        Normal
## 1305   <NA>  <NA>        <NA>       0      6   2007       WD        Normal
## 1311   <NA>  <NA>        <NA>       0      8   2006       WD        Normal
## 1314   <NA>  <NA>        <NA>       0      7   2008       WD        Normal
## 1335   <NA>  <NA>        <NA>       0      8   2007       WD        Normal
## 1339   <NA>  <NA>        <NA>       0      6   2006       WD        Normal
## 1341   <NA>  <NA>        <NA>       0      8   2007       WD        Normal
## 1347   <NA>  <NA>        <NA>       0      6   2008       WD        Normal
## 1349   <NA>  GdWo        <NA>       0     10   2008       WD        Normal
## 1350   <NA>  <NA>        <NA>       0      4   2010       WD        Normal
## 1351   <NA>  <NA>        <NA>       0      6   2010       WD        Normal
## 1355   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1358   <NA>  <NA>        <NA>       0      1   2010       WD        Normal
## 1361   <NA>  <NA>        <NA>       0      6   2009       WD        Normal
## 1366   <NA>  <NA>        <NA>       0      3   2007       WD        Normal
## 1373   <NA>  <NA>        <NA>       0      5   2008       WD        Normal
## 1375   <NA>  <NA>        <NA>       0      8   2007       WD        Normal
## 1388   <NA>  <NA>        <NA>       0      6   2010       WD        Normal
## 1399   <NA> MnPrv        <NA>       0      3   2009       WD        Normal
## 1409   <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 1411   <NA>  <NA>        <NA>       0     12   2006       WD        Normal
## 1415     Gd GdPrv        <NA>       0      8   2006       WD        Alloca
## 1416   <NA>  GdWo        <NA>       0      6   2007       WD        Normal
## 1421   <NA>  <NA>        <NA>       0      4   2007       WD        Normal
## 1423   <NA>  <NA>        <NA>       0     10   2009       WD        Normal
## 1433   <NA>  <NA>        <NA>       0      5   2008       WD        Normal
## 1435   <NA>  <NA>        <NA>       0      5   2009       WD        Normal
## 1438   <NA>  <NA>        <NA>       0      4   2010       WD        Normal
##      SalePrice
## 8       200000
## 13      144000
## 15      157000
## 17      149000
## 25      154000
## 32      149350
## 43      144000
## 44      130250
## 51      177000
## 65      219500
## 67      180000
## 77      135750
## 85      168500
## 96      185000
## 101     205000
## 105     169500
## 112     180000
## 114     217000
## 117     139000
## 121     180000
## 127     128000
## 132     244000
## 134     220000
## 137     143000
## 148     222500
## 150     115000
## 153     190000
## 154     235000
## 161     162500
## 167     190000
## 170     228000
## 171     128500
## 178     172500
## 181     177000
## 187     173000
## 192     184000
## 204     149000
## 208     141000
## 209     277000
## 215     161750
## 219     311500
## 222     200000
## 237     194500
## 244     205000
## 249     277000
## 269     148000
## 287      88000
## 288     122000
## 293     235000
## 307      89500
## 308      82500
## 310     165600
## 319     187500
## 328     214500
## 330     119000
## 335     228950
## 342      87500
## 346     151500
## 347     157500
## 351     190000
## 356     173000
## 360     156000
## 361     145000
## 364     190000
## 366     159000
## 369     162000
## 370     172400
## 375      61000
## 384     240000
## 392     106500
## 393     100000
## 404     168000
## 405     150000
## 412     222000
## 421     215000
## 426     275000
## 447     199900
## 452     204000
## 457     256000
## 458     161000
## 459     110000
## 465     178740
## 470     212000
## 484     132500
## 490     115000
## 496     430000
## 516     158000
## 518     211000
## 536     111250
## 537     158000
## 538     272000
## 540     248000
## 544     229000
## 558     234000
## 559     121500
## 563     268000
## 568     135960
## 579     181900
## 592     140000
## 609     313000
## 610     148000
## 611     261500
## 615     183200
## 622     168500
## 625     139900
## 640     226000
## 644     143250
## 658     197900
## 664     129000
## 666     168000
## 670     165000
## 677     128500
## 680     173000
## 683     207500
## 685     148800
## 688     141000
## 704     302000
## 707     109900
## 712     130500
## 718     275000
## 719     143000
## 724     222000
## 732     108000
## 743     299800
## 744     236000
## 749     162000
## 755     158900
## 768     134900
## 781     165500
## 783     161500
## 787     187500
## 789     146800
## 792     194500
## 809     144500
## 814     137000
## 815     271000
## 820     225000
## 826     185000
## 838     140000
## 843     171000
## 849     215000
## 851     158000
## 853     127000
## 854     147000
## 857     250000
## 863     148500
## 866     169000
## 877     136500
## 880     178000
## 891     165000
## 898     110000
## 902     125500
## 906     131000
## 909     143500
## 915     135000
## 923     175000
## 925     176000
## 926     236500
## 927     222000
## 936     244400
## 938     214000
## 941     137500
## 950     172000
## 958     272000
## 964     135000
## 971     165000
## 975     178400
## 978     255900
## 983     195000
## 991     136500
## 992     185000
## 998     136905
## 1001    163500
## 1012    187500
## 1013    160000
## 1019    287000
## 1025    160000
## 1027    310000
## 1028    230000
## 1030     84000
## 1032    287000
## 1036    173000
## 1040    139600
## 1052    248000
## 1054    220000
## 1059    154000
## 1072    138800
## 1079    187500
## 1081     83500
## 1092    170000
## 1103    181000
## 1105    188000
## 1111    184100
## 1117    112000
## 1119    163900
## 1133    196000
## 1136    197500
## 1138     80000
## 1141    180000
## 1143    116900
## 1148    120500
## 1149    201800
## 1156    224000
## 1159    194000
## 1172    115000
## 1175    250000
## 1185    168000
## 1188    165000
## 1201    107000
## 1208    145000
## 1225    190000
## 1228    142000
## 1238    230000
## 1241    169900
## 1245    171750
## 1247    294000
## 1254    181000
## 1256    161500
## 1262    381000
## 1264    260000
## 1265    185750
## 1266    137000
## 1270    162000
## 1271    197900
## 1279    143000
## 1280    190000
## 1283    180500
## 1293    225000
## 1294    177500
## 1302    179200
## 1305    302000
## 1311    275000
## 1314     72500
## 1335    228500
## 1339    262500
## 1341    215000
## 1347    235000
## 1349    110000
## 1350    149900
## 1351    177500
## 1355    104900
## 1358    216000
## 1361    144000
## 1366    466500
## 1373    237500
## 1375    112000
## 1388    160000
## 1399    112000
## 1409    340000
## 1411    223000
## 1415    274970
## 1416    144000
## 1421    182900
## 1423    143750
## 1433    149300
## 1435    121000
## 1438    157900

A total of 257 rows contain NA values, this constitutes 17.7% of the data, after dropping 9 rows from NA’s in Elctrical & MasVnrType

Data Exploration

8.

hist(housing$SalePrice)

Sales Price is right-skewed, so the mean is greater than the median.

9.

plot(SalePrice~., data=housing)

Sales Price appears to have correlation with: MSZoning (specifically Residential Low Density seems to correlate with higher prices), Street (Paved = higher prices), Alley (homes with no Alley or paved Alley have higher prices), Neighborhood (specific neighborhoods correlate with higher prices), Condition2 (Adjacent or near off-site features correlate with higher prices), BldgType, OverallQual, OverallCond, YearBuilt, YearRemodAdd (may be a better variable than YearBuilt, since remodel age = year built if no remodel has occured), RoofMatl, ExterQual, BsmtQual, BsmtExposure, BsmtFinType1, BstFinSF1, TotalBsmtSF, HeatingQC, CentralAir, Electrical, 1stFlrSF, 2ndFlrSF, GrLivArea, FullBath, KitchenQual, TotRmsAbvGrd, FireplaceQu, GarageType, GarageFinish, GarageCars, GarageArea, GarageQual, GarageCond, PavedDrive, PoolQC, SaleType, SaleCondition

10.

library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
set.seed(123)
intrain <- createDataPartition(housing$SalePrice, p = .80, list = FALSE)
housing.train <- housing[intrain, ]
housing.test <- housing[-intrain, ]

Creating Predictive Models

11.

library(glmnet)
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
## Loaded glmnet 4.1-4
set.seed(1)
lasso <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 1, lambda = 10^seq(-3,3, length = 100)))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
coef(lasso$finalModel, lasso$bestTune$lambda)
## 275 x 1 sparse Matrix of class "dgCMatrix"
##                                 s1
## (Intercept)           1.808748e+05
## MSSubClass           -4.855969e+03
## MSZoningFV            .           
## MSZoningRH            .           
## MSZoningRL            1.901305e+03
## MSZoningRM            .           
## LotFrontage           .           
## LotArea               9.135331e+02
## StreetPave            1.203417e+03
## AlleyPave             .           
## AlleyNA               .           
## LotShapeIR2           1.293812e+03
## LotShapeIR3          -2.428685e+03
## LotShapeReg           .           
## LandContourHLS        .           
## LandContourLow        .           
## LandContourLvl        1.693954e+02
## UtilitiesNoSeWa      -6.794625e+02
## LotConfigCulDSac      2.712483e+03
## LotConfigFR2          .           
## LotConfigFR3         -1.943216e+02
## LotConfigInside       .           
## LandSlopeMod          3.453039e+01
## LandSlopeSev         -3.111790e+02
## NeighborhoodBlueste   .           
## NeighborhoodBrDale    .           
## NeighborhoodBrkSide   1.035020e+03
## NeighborhoodClearCr   1.002119e+03
## NeighborhoodCollgCr   .           
## NeighborhoodCrawfor   4.474473e+03
## NeighborhoodEdwards  -1.468655e+03
## NeighborhoodGilbert   .           
## NeighborhoodIDOTRR    .           
## NeighborhoodMeadowV  -2.696641e+02
## NeighborhoodMitchel   .           
## NeighborhoodNAmes     .           
## NeighborhoodNoRidge   5.081346e+03
## NeighborhoodNPkVill   .           
## NeighborhoodNridgHt   5.670871e+03
## NeighborhoodNWAmes   -6.888416e+02
## NeighborhoodOldTown  -5.825281e+01
## NeighborhoodSawyer    .           
## NeighborhoodSawyerW   9.118547e+02
## NeighborhoodSomerst   2.970225e+03
## NeighborhoodStoneBr   5.203892e+03
## NeighborhoodSWISU    -8.703419e+01
## NeighborhoodTimber    .           
## NeighborhoodVeenker   1.012276e+02
## Condition1Feedr      -4.186221e+02
## Condition1Norm        2.991711e+03
## Condition1PosA        .           
## Condition1PosN        .           
## Condition1RRAe       -7.485716e+02
## Condition1RRAn        4.974039e+02
## Condition1RRNe        .           
## Condition1RRNn        .           
## Condition2Feedr       .           
## Condition2Norm        .           
## Condition2PosA        1.434151e+02
## Condition2PosN       -9.870577e+03
## Condition2RRAe       -1.367026e+00
## Condition2RRAn        .           
## Condition2RRNn        .           
## BldgType2fmCon        1.949780e+02
## BldgTypeDuplex       -1.433948e+03
## BldgTypeTwnhs        -1.125001e+03
## BldgTypeTwnhsE       -9.260561e+02
## HouseStyle1.5Unf      3.735028e+02
## HouseStyle1Story      .           
## HouseStyle2.5Fin     -7.490851e+02
## HouseStyle2.5Unf      .           
## HouseStyle2Story      .           
## HouseStyleSFoyer      .           
## HouseStyleSLvl        2.952193e+02
## OverallQual2          .           
## OverallQual3         -5.463212e+02
## OverallQual4         -7.887650e+02
## OverallQual5         -6.503855e+02
## OverallQual6          .           
## OverallQual7          2.123743e+03
## OverallQual8          8.359035e+03
## OverallQual9          1.256765e+04
## OverallQual10         6.564475e+03
## OverallCond2         -1.111678e+02
## OverallCond3         -2.649090e+03
## OverallCond4         -1.394846e+03
## OverallCond5         -2.711153e+03
## OverallCond6          .           
## OverallCond7          1.667515e+03
## OverallCond8          7.328050e+02
## OverallCond9          1.116484e+03
## YearBuilt             6.972904e+03
## YearRemodAdd          2.300398e+03
## RoofStyleGable       -1.923172e+01
## RoofStyleGambrel      .           
## RoofStyleHip          .           
## RoofStyleMansard      2.641756e+02
## RoofStyleShed         1.730142e+02
## RoofMatlCompShg       1.891926e+03
## RoofMatlMembran       8.361410e+02
## RoofMatlMetal         .           
## RoofMatlRoll          .           
## RoofMatlTar&Grv       8.137608e+01
## RoofMatlWdShake       4.877634e+02
## RoofMatlWdShngl       6.634192e+03
## Exterior1stAsphShn    .           
## Exterior1stBrkComm    .           
## Exterior1stBrkFace    2.624487e+03
## Exterior1stCBlock     .           
## Exterior1stCemntBd    1.638894e+03
## Exterior1stHdBoard   -2.902244e+02
## Exterior1stImStucc    .           
## Exterior1stMetalSd    .           
## Exterior1stPlywood    1.449538e+02
## Exterior1stStone      .           
## Exterior1stStucco     .           
## Exterior1stVinylSd    .           
## Exterior1stWd Sdng    .           
## Exterior1stWdShing   -2.258862e+02
## Exterior2ndAsphShn    .           
## Exterior2ndBrk Cmn    .           
## Exterior2ndBrkFace    .           
## Exterior2ndCBlock     .           
## Exterior2ndCmentBd    .           
## Exterior2ndHdBoard    .           
## Exterior2ndImStucc    .           
## Exterior2ndMetalSd    .           
## Exterior2ndOther     -3.133446e+02
## Exterior2ndPlywood    .           
## Exterior2ndStone      .           
## Exterior2ndStucco    -9.748898e+02
## Exterior2ndVinylSd    8.582581e+02
## Exterior2ndWd Sdng    .           
## Exterior2ndWd Shng   -2.570788e+02
## MasVnrTypeBrkFace    -6.407364e+02
## MasVnrTypeNone        .           
## MasVnrTypeStone       2.896943e+02
## MasVnrArea            2.756441e+03
## ExterQualFa          -1.165190e+02
## ExterQualGd           .           
## ExterQualTA          -2.068488e+03
## ExterCondFa           .           
## ExterCondGd           .           
## ExterCondPo          -3.420444e+02
## ExterCondTA           3.438523e+02
## FoundationCBlock      .           
## FoundationPConc       .           
## FoundationSlab       -2.966875e+02
## FoundationStone       9.658727e+01
## FoundationWood       -5.376220e+02
## BsmtQualFa            .           
## BsmtQualGd           -2.699710e+03
## BsmtQualTA           -1.559807e+03
## BsmtQualNA           -4.356053e+02
## BsmtCondGd            .           
## BsmtCondPo           -5.093603e+02
## BsmtCondTA            8.162638e+02
## BsmtCondNA           -6.576593e+00
## BsmtExposureGd        5.699746e+03
## BsmtExposureMn        .           
## BsmtExposureNo       -2.342891e+03
## BsmtExposureNA       -1.249622e+03
## BsmtFinType1BLQ       .           
## BsmtFinType1GLQ       2.460948e+03
## BsmtFinType1LwQ       .           
## BsmtFinType1Rec       .           
## BsmtFinType1Unf      -1.335871e+03
## BsmtFinType1NA       -2.801005e+02
## BsmtFinSF1            2.520294e+03
## BsmtFinType2BLQ       .           
## BsmtFinType2GLQ       5.634077e+02
## BsmtFinType2LwQ       .           
## BsmtFinType2Rec      -2.006174e+02
## BsmtFinType2Unf       .           
## BsmtFinType2NA        .           
## BsmtFinSF2            .           
## BsmtUnfSF             .           
## TotalBsmtSF           1.388839e+03
## HeatingGasA           .           
## HeatingGasW           .           
## HeatingGrav           .           
## HeatingOthW          -9.819097e+02
## HeatingWall           .           
## HeatingQCFa           .           
## HeatingQCGd          -4.288629e+02
## HeatingQCPo           .           
## HeatingQCTA          -8.634036e+02
## CentralAirY           1.085297e+02
## ElectricalFuseF       .           
## ElectricalFuseP       .           
## ElectricalMix         .           
## ElectricalSBrkr       .           
## X1stFlrSF             9.851853e+02
## X2ndFlrSF             .           
## LowQualFinSF         -7.377839e+02
## GrLivArea             2.569158e+04
## BsmtFullBath          2.920552e+03
## BsmtHalfBath          4.738209e+01
## FullBath              4.039362e+03
## HalfBath              1.094228e+01
## BedroomAbvGr         -5.372127e+02
## KitchenAbvGr         -2.773361e+03
## KitchenQualFa        -1.137243e+03
## KitchenQualGd        -3.928501e+03
## KitchenQualTA        -4.775088e+03
## TotRmsAbvGrd          2.099617e+03
## FunctionalMaj2       -8.746126e+01
## FunctionalMin1       -3.870090e+00
## FunctionalMin2        .           
## FunctionalMod         .           
## FunctionalSev        -9.239911e+02
## FunctionalTyp         2.876395e+03
## Fireplaces            8.452490e+02
## FireplaceQuFa         .           
## FireplaceQuGd         1.190241e+02
## FireplaceQuPo         .           
## FireplaceQuTA        -4.473275e+02
## FireplaceQuNA        -2.581906e+03
## GarageTypeAttchd      .           
## GarageTypeBasment    -6.211299e+02
## GarageTypeBuiltIn     1.374027e+03
## GarageTypeCarPort     .           
## GarageTypeDetchd      .           
## GarageTypeNA          .           
## GarageYrBlt           .           
## GarageFinishRFn       .           
## GarageFinishUnf       .           
## GarageFinishNA        .           
## GarageCars            6.930239e+03
## GarageArea            1.593557e+01
## GarageQualFa         -7.738352e+02
## GarageQualGd          7.220481e+02
## GarageQualPo          .           
## GarageQualTA          .           
## GarageQualNA          .           
## GarageCondFa         -2.990867e+02
## GarageCondGd          .           
## GarageCondPo          .           
## GarageCondTA          .           
## GarageCondNA          .           
## PavedDriveP           .           
## PavedDriveY           5.107758e+02
## WoodDeckSF            1.605118e+03
## OpenPorchSF           1.106775e+03
## EnclosedPorch         3.298012e+02
## X3SsnPorch            5.028829e+02
## ScreenPorch           2.176282e+03
## PoolArea              1.739860e+04
## PoolQCFa             -9.467740e+03
## PoolQCGd             -1.705031e+04
## PoolQCNA              .           
## FenceGdWo            -5.559720e+02
## FenceMnPrv           -1.517562e+02
## FenceMnWw            -5.781862e+00
## FenceNA               .           
## MiscFeatureOthr       .           
## MiscFeatureShed       .           
## MiscFeatureTenC       .           
## MiscFeatureNA         .           
## MiscVal               .           
## MoSold               -7.637603e+02
## YrSold                .           
## SaleTypeCon           9.338769e+02
## SaleTypeConLD         .           
## SaleTypeConLI         .           
## SaleTypeConLw         .           
## SaleTypeCWD           6.399739e-01
## SaleTypeNew           7.686916e+03
## SaleTypeOth           .           
## SaleTypeWD            .           
## SaleConditionAdjLand  3.888843e+02
## SaleConditionAlloca   6.327893e+02
## SaleConditionFamily  -4.075413e+01
## SaleConditionNormal   1.995598e+03
## SaleConditionPartial  .
predictions.lasso <- predict(lasso, housing.test, na.action = na.pass)
RMSE(predictions.lasso, housing.test$SalePrice)
## [1] 34113.53

Several variable coefficients were shrunk to zero, meaning that they were not used for this prediction model. RMSE = 34113.53

12.

set.seed(1)
ridge <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = 10^seq(-3,3, length = 100)))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
predictions.ridge <- predict(ridge, housing.test, na.action = na.pass)
RMSE(predictions.ridge, housing.test$SalePrice)
## [1] 32406.35

RMSE = 32406.35

13.

set.seed(1)
enet <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "glmnet", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = seq(0, 1, length = 10), lambda = 10^seq(-3,3, length = 100)))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
predictions.enet <- predict(enet, housing.test, na.action = na.pass)
RMSE(predictions.enet, housing.test$SalePrice)
## [1] 32406.35

RMSE = 32406.35; which is identical to ridge; which indicates that an alpha of 1 was the best alpha used and optimal lamda was the same for ridge as enet.

14.

set.seed(1)
rf <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, importance = T, method = "rf", metric = "RMSE", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(mtry = c(5, 15, 30, 60, 79)))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev

## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
predictions.rf <- predict(rf, housing.test, na.action = na.pass)
RMSE(predictions.rf, housing.test$SalePrice)
## [1] 26146.37
varImp(rf)
## rf variable importance
## 
##   only 20 most important variables shown (out of 274)
## 
##               Overall
## GrLivArea      100.00
## TotalBsmtSF     61.99
## X2ndFlrSF       56.96
## X1stFlrSF       52.07
## GarageArea      48.38
## LotArea         47.11
## YearBuilt       46.92
## YearRemodAdd    45.05
## GarageCars      45.01
## ExterQualTA     44.69
## Fireplaces      42.11
## BsmtFinSF1      40.47
## FireplaceQuNA   40.00
## GarageYrBlt     39.89
## OverallQual7    38.42
## MSZoningRL      37.41
## KitchenQualTA   37.12
## FullBath        36.76
## KitchenQualGd   36.59
## MSSubClass      36.55

RMSE 26146.37

The variables: GrLivArea, TotalBsmtSF, X2ndFlrSF, X1stFlrSF, GarageArea, LotArea, YearBuilt, YearRemodAdd, GarageCars, and ExterQualTA were the 10 most predictive variables.

15.

set.seed(1)
gbm <- train(SalePrice ~ ., data = housing.train,  preProc = "nzv", na.action = na.pass, method = "gbm", trControl = trainControl("cv", number = 10))
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5810517523.4057            -nan     0.1000 412206625.2410
##      2 5418794652.7246            -nan     0.1000 367903339.1821
##      3 5101203039.4251            -nan     0.1000 282547983.2481
##      4 4776626098.1778            -nan     0.1000 301721161.9660
##      5 4477609190.4700            -nan     0.1000 284014279.7840
##      6 4231343318.6250            -nan     0.1000 195158781.9390
##      7 3977786668.2257            -nan     0.1000 234747904.8043
##      8 3759492265.4612            -nan     0.1000 222619710.9716
##      9 3579609849.8262            -nan     0.1000 179512399.0900
##     10 3419346423.7009            -nan     0.1000 160187693.9525
##     20 2313241552.3796            -nan     0.1000 79828744.0008
##     40 1433471318.6721            -nan     0.1000 18914487.5603
##     60 1143834913.8656            -nan     0.1000 -2658220.6605
##     80 1023183005.4850            -nan     0.1000 4306459.3543
##    100 957836892.5763            -nan     0.1000 -19801963.9460
##    120 901092450.9095            -nan     0.1000 -10308140.7067
##    140 865039959.0498            -nan     0.1000 -1054613.2748
##    150 856506078.4405            -nan     0.1000 -12190924.2521
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5620611416.5482            -nan     0.1000 599917625.7505
##      2 5137946101.2975            -nan     0.1000 420877286.7205
##      3 4654762764.3019            -nan     0.1000 438202431.1373
##      4 4283144551.1734            -nan     0.1000 386876021.9633
##      5 3955196721.1058            -nan     0.1000 302784481.0819
##      6 3696440149.0543            -nan     0.1000 236531393.9556
##      7 3384204915.7867            -nan     0.1000 286696598.1948
##      8 3160573765.0286            -nan     0.1000 240312496.5710
##      9 2974748663.5895            -nan     0.1000 195376392.4608
##     10 2794410176.8200            -nan     0.1000 172435698.8097
##     20 1651427715.7795            -nan     0.1000 65014368.5059
##     40 1054662953.4832            -nan     0.1000 -3018825.9277
##     60 858099948.1010            -nan     0.1000 2912141.7942
##     80 769958965.2351            -nan     0.1000 -1122926.8329
##    100 696077517.5866            -nan     0.1000 -4848016.5611
##    120 646163156.1802            -nan     0.1000 1631823.8846
##    140 602423331.5376            -nan     0.1000 -3109313.5926
##    150 589343587.3863            -nan     0.1000 -9255723.4399
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5517418278.0121            -nan     0.1000 729202838.5460
##      2 4955051879.9838            -nan     0.1000 519157110.7618
##      3 4473164300.1120            -nan     0.1000 521166451.0027
##      4 4047446208.6388            -nan     0.1000 403156289.8010
##      5 3660158899.4957            -nan     0.1000 339765601.1908
##      6 3347051597.7590            -nan     0.1000 252130820.0807
##      7 3050084479.5011            -nan     0.1000 248880501.1887
##      8 2818844839.5564            -nan     0.1000 231523845.5054
##      9 2594990838.3118            -nan     0.1000 196623509.9012
##     10 2402351294.7681            -nan     0.1000 166308496.8176
##     20 1355527258.4092            -nan     0.1000 39333664.4248
##     40 848960053.5965            -nan     0.1000 3531196.7250
##     60 691418996.7677            -nan     0.1000 -4176294.8259
##     80 608269528.1309            -nan     0.1000 -7369204.3735
##    100 534862826.0827            -nan     0.1000 -1684989.8913
##    120 493908562.0640            -nan     0.1000 -3374722.6912
##    140 446960423.2994            -nan     0.1000 -2784207.8908
##    150 430697169.1151            -nan     0.1000 -2987867.2184
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6160592621.9610            -nan     0.1000 440980474.3094
##      2 5721062572.8025            -nan     0.1000 468108248.2007
##      3 5377056260.9103            -nan     0.1000 339473195.9387
##      4 5050393454.8901            -nan     0.1000 333211984.0022
##      5 4757841198.6609            -nan     0.1000 273548116.1285
##      6 4508356135.4049            -nan     0.1000 212047905.8927
##      7 4253928376.7478            -nan     0.1000 257245084.9928
##      8 4027555206.4180            -nan     0.1000 211904349.6546
##      9 3816184423.1873            -nan     0.1000 191675350.6181
##     10 3608336703.3204            -nan     0.1000 193272327.8169
##     20 2401865596.2038            -nan     0.1000 87088274.2661
##     40 1488204686.1044            -nan     0.1000 23197036.5846
##     60 1172838509.9474            -nan     0.1000 8260766.1687
##     80 1052457690.0236            -nan     0.1000 -1890633.7563
##    100 994905336.8291            -nan     0.1000 -6034837.7659
##    120 951020629.0980            -nan     0.1000 1114312.0158
##    140 915592506.9078            -nan     0.1000 -4679668.1222
##    150 897556229.4320            -nan     0.1000 -10875240.5056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5932896052.0099            -nan     0.1000 623229458.5710
##      2 5438051872.0562            -nan     0.1000 482736242.4847
##      3 4940288251.5179            -nan     0.1000 421052169.8095
##      4 4526453133.6632            -nan     0.1000 364627391.0810
##      5 4115946303.6636            -nan     0.1000 376356825.6445
##      6 3813151557.8601            -nan     0.1000 199197315.8465
##      7 3525982767.6679            -nan     0.1000 241109356.4354
##      8 3268871644.6728            -nan     0.1000 255921053.4491
##      9 3049402668.2721            -nan     0.1000 207305046.1762
##     10 2854540686.1239            -nan     0.1000 164955618.1738
##     20 1750638188.9392            -nan     0.1000 71096223.6568
##     40 1069532445.2089            -nan     0.1000 4550131.7524
##     60 894252109.4196            -nan     0.1000 -5056589.1692
##     80 802634388.9929            -nan     0.1000 -5314942.1957
##    100 748952348.1138            -nan     0.1000 1580061.7632
##    120 703082991.5817            -nan     0.1000 -6210867.3433
##    140 653207184.3743            -nan     0.1000 503726.2780
##    150 623916444.8743            -nan     0.1000 -10469170.8572
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5873667976.7261            -nan     0.1000 699339729.0287
##      2 5257044053.8921            -nan     0.1000 562590660.3514
##      3 4759751201.2593            -nan     0.1000 459013496.7967
##      4 4314009656.3604            -nan     0.1000 495251224.8407
##      5 3918265084.7000            -nan     0.1000 398207708.1125
##      6 3556425515.6325            -nan     0.1000 279513907.7444
##      7 3248146267.6559            -nan     0.1000 256399912.2598
##      8 2988682621.7603            -nan     0.1000 166034171.0693
##      9 2748933634.1319            -nan     0.1000 217082741.8684
##     10 2542309929.0197            -nan     0.1000 179462099.7996
##     20 1419021454.6024            -nan     0.1000 39941999.6133
##     40 900892035.9553            -nan     0.1000 5280947.7033
##     60 742729530.6835            -nan     0.1000 -6376590.1733
##     80 650071632.5343            -nan     0.1000 6133130.1785
##    100 561974145.3448            -nan     0.1000 -3444218.8833
##    120 509443855.6271            -nan     0.1000 -4712274.7187
##    140 476684210.9352            -nan     0.1000 295956.3803
##    150 454116002.2801            -nan     0.1000 -3382557.4760
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5854177546.9492            -nan     0.1000 411426808.7938
##      2 5467179066.5729            -nan     0.1000 367530678.1610
##      3 5099913719.5577            -nan     0.1000 356289670.6169
##      4 4812789684.2725            -nan     0.1000 297510644.9878
##      5 4532902023.6949            -nan     0.1000 282936660.3958
##      6 4274804392.8882            -nan     0.1000 252171188.3030
##      7 4050933350.4951            -nan     0.1000 213557624.9716
##      8 3795699047.6249            -nan     0.1000 205952599.3702
##      9 3613958853.4779            -nan     0.1000 173126567.8681
##     10 3437009008.1595            -nan     0.1000 167308480.5086
##     20 2280141171.1667            -nan     0.1000 59833351.5156
##     40 1380929195.7622            -nan     0.1000 3541053.1667
##     60 1077191987.2629            -nan     0.1000 7673119.9089
##     80 945945198.4697            -nan     0.1000 -2722636.5633
##    100 863146245.0508            -nan     0.1000 -8336866.7437
##    120 826777446.6781            -nan     0.1000 -9047415.1484
##    140 795933753.3884            -nan     0.1000 -4302703.0416
##    150 785176765.9823            -nan     0.1000 372388.4895
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5736187185.9915            -nan     0.1000 471496778.8048
##      2 5138000641.2040            -nan     0.1000 605224389.1727
##      3 4642704103.5073            -nan     0.1000 523150625.1409
##      4 4222691961.0090            -nan     0.1000 416731612.2015
##      5 3849239919.7670            -nan     0.1000 333097803.9065
##      6 3494388893.9883            -nan     0.1000 324989727.3128
##      7 3225052644.6825            -nan     0.1000 252948456.8457
##      8 3005787386.3780            -nan     0.1000 209779818.0370
##      9 2810557141.2837            -nan     0.1000 189266161.6836
##     10 2612047504.4781            -nan     0.1000 191810167.5283
##     20 1545720657.3578            -nan     0.1000 55883087.8347
##     40 964693270.3917            -nan     0.1000 9798297.3384
##     60 811668771.7798            -nan     0.1000 -241604.2514
##     80 728811876.3073            -nan     0.1000 -3817861.6901
##    100 657856228.5770            -nan     0.1000 -4206200.7686
##    120 611141389.2523            -nan     0.1000 -2774582.1833
##    140 579147083.7213            -nan     0.1000 -4326920.1865
##    150 560774037.5304            -nan     0.1000 -676352.9107
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5593116286.3290            -nan     0.1000 569468951.4793
##      2 5038768828.0325            -nan     0.1000 551580478.6502
##      3 4509650236.2430            -nan     0.1000 485479726.8933
##      4 4055592436.0314            -nan     0.1000 482690617.0540
##      5 3644329456.9311            -nan     0.1000 332883479.5129
##      6 3301305332.8843            -nan     0.1000 303295454.7224
##      7 3050909196.8196            -nan     0.1000 205657237.1342
##      8 2790592673.6670            -nan     0.1000 232997090.9615
##      9 2572328527.4820            -nan     0.1000 203848592.9195
##     10 2373081782.9094            -nan     0.1000 190721377.0404
##     20 1304881170.2175            -nan     0.1000 43886477.7512
##     40 781296089.3811            -nan     0.1000 4009959.1289
##     60 649796140.9502            -nan     0.1000 -10433171.3192
##     80 573744533.2247            -nan     0.1000 -5846268.9742
##    100 528855676.1426            -nan     0.1000 -3702813.5419
##    120 491809702.2441            -nan     0.1000 -4310375.2433
##    140 468101622.4865            -nan     0.1000 -3100111.2709
##    150 448695219.6123            -nan     0.1000 -4086275.7459
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5847341678.8806            -nan     0.1000 421720505.1755
##      2 5452788520.8510            -nan     0.1000 430409585.2569
##      3 5119826590.5870            -nan     0.1000 342900553.2067
##      4 4806151223.9645            -nan     0.1000 268503305.8967
##      5 4530039305.8279            -nan     0.1000 247894363.5592
##      6 4262365253.1509            -nan     0.1000 253327629.5427
##      7 4043287335.2893            -nan     0.1000 210438761.6338
##      8 3813074071.0217            -nan     0.1000 214160571.9842
##      9 3632046358.5677            -nan     0.1000 181978421.6231
##     10 3469624686.1961            -nan     0.1000 168242020.7860
##     20 2337394428.9587            -nan     0.1000 67901921.1504
##     40 1483985205.7337            -nan     0.1000 22898100.9435
##     60 1183536372.2427            -nan     0.1000 278394.4829
##     80 1059742621.1655            -nan     0.1000 -8166767.4641
##    100 992871734.0824            -nan     0.1000 -6287229.8913
##    120 943305822.6502            -nan     0.1000 -7174664.3368
##    140 898931086.0325            -nan     0.1000 132999.9586
##    150 881799802.9681            -nan     0.1000 1577581.1737
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5627890743.0495            -nan     0.1000 662855129.2384
##      2 5144482338.1345            -nan     0.1000 477226554.1946
##      3 4716882828.3830            -nan     0.1000 393360596.6879
##      4 4384393764.2846            -nan     0.1000 258086653.6198
##      5 4042772821.8348            -nan     0.1000 313252427.0590
##      6 3680050041.0097            -nan     0.1000 297280001.8267
##      7 3427412222.3900            -nan     0.1000 164387337.7731
##      8 3174057338.9701            -nan     0.1000 217509651.0387
##      9 2957871581.9347            -nan     0.1000 178625766.3336
##     10 2781334688.0406            -nan     0.1000 154878100.0353
##     20 1627830372.0761            -nan     0.1000 60664375.6110
##     40 1059486881.0296            -nan     0.1000 2399794.1625
##     60 884896419.7912            -nan     0.1000 -6273153.7402
##     80 781338792.0094            -nan     0.1000 -4460274.1577
##    100 721297604.7092            -nan     0.1000 -6256385.4306
##    120 666567910.2838            -nan     0.1000 -2355688.5252
##    140 624707924.5357            -nan     0.1000 -3073769.5811
##    150 608266028.2253            -nan     0.1000 -2934234.4090
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5592830553.3143            -nan     0.1000 655593167.8272
##      2 5007981719.9299            -nan     0.1000 591094740.8248
##      3 4521829360.3523            -nan     0.1000 405214014.1913
##      4 4119851576.8613            -nan     0.1000 382786912.1430
##      5 3733827078.0281            -nan     0.1000 386668128.0735
##      6 3396530984.0230            -nan     0.1000 317129271.1247
##      7 3100977467.2646            -nan     0.1000 290515522.6069
##      8 2826817100.9276            -nan     0.1000 163894689.3521
##      9 2622409965.5217            -nan     0.1000 168871313.3858
##     10 2427795908.1872            -nan     0.1000 174375875.2713
##     20 1370651142.8774            -nan     0.1000 49013270.9160
##     40 831009793.9885            -nan     0.1000 5772235.6090
##     60 690239311.5191            -nan     0.1000 -4925737.2273
##     80 606994504.5978            -nan     0.1000 912882.8359
##    100 540071846.1607            -nan     0.1000 -2483141.5081
##    120 485988092.0815            -nan     0.1000 -1671109.9916
##    140 450962745.8178            -nan     0.1000 -3186404.0482
##    150 431364062.1999            -nan     0.1000 -484786.7896
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5913759504.1483            -nan     0.1000 379316549.4356
##      2 5532270197.9776            -nan     0.1000 346078481.9455
##      3 5176062580.9699            -nan     0.1000 337816217.7217
##      4 4850335816.3248            -nan     0.1000 310247732.6538
##      5 4561926814.1061            -nan     0.1000 286554598.5713
##      6 4285720037.4924            -nan     0.1000 265121434.5288
##      7 4065401455.6381            -nan     0.1000 186523332.4169
##      8 3849159915.3790            -nan     0.1000 201998823.1491
##      9 3651364627.3071            -nan     0.1000 178330139.4247
##     10 3471790358.6992            -nan     0.1000 167343959.0756
##     20 2335119064.1154            -nan     0.1000 57546000.1150
##     40 1490729902.7350            -nan     0.1000 14413224.5154
##     60 1193541623.2322            -nan     0.1000 1516321.0805
##     80 1081165111.9317            -nan     0.1000 4564851.1559
##    100 1009774288.0583            -nan     0.1000 -4814511.8660
##    120 972984578.8250            -nan     0.1000 -10186891.7982
##    140 937479690.4936            -nan     0.1000 -4152431.1814
##    150 923495522.6108            -nan     0.1000 1601959.2728
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5770247497.6126            -nan     0.1000 631822780.8018
##      2 5232692592.3193            -nan     0.1000 569469574.7582
##      3 4756476555.9040            -nan     0.1000 451361847.8192
##      4 4331646557.5298            -nan     0.1000 448707521.2263
##      5 4022739862.1484            -nan     0.1000 316396278.0784
##      6 3691672362.9062            -nan     0.1000 315651678.7719
##      7 3430416882.0506            -nan     0.1000 201383846.0448
##      8 3193170850.6813            -nan     0.1000 223174168.9583
##      9 2958787661.3042            -nan     0.1000 198501427.2016
##     10 2779420917.1476            -nan     0.1000 185447238.7936
##     20 1663709597.3424            -nan     0.1000 62927072.8980
##     40 1074476172.0298            -nan     0.1000 -193662.8415
##     60 905458131.0277            -nan     0.1000 -14006322.5989
##     80 808665487.9780            -nan     0.1000 1255365.9629
##    100 737975677.6894            -nan     0.1000 -8784102.0592
##    120 685017592.9054            -nan     0.1000 -6380446.8780
##    140 641859995.4666            -nan     0.1000 -4809223.3637
##    150 620769988.9924            -nan     0.1000 414814.8601
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5670737667.8657            -nan     0.1000 704548445.2034
##      2 5079687418.9972            -nan     0.1000 542647052.0319
##      3 4573343679.8666            -nan     0.1000 437282872.7705
##      4 4113164494.6911            -nan     0.1000 359661430.9168
##      5 3730486995.9999            -nan     0.1000 368221395.7069
##      6 3398266144.2765            -nan     0.1000 344089466.4962
##      7 3116286670.8807            -nan     0.1000 217654013.5156
##      8 2863788181.6559            -nan     0.1000 250560033.0567
##      9 2641457178.3974            -nan     0.1000 211675714.6667
##     10 2444113564.4301            -nan     0.1000 144445221.1099
##     20 1422821734.9350            -nan     0.1000 37775664.1424
##     40 902576920.4174            -nan     0.1000 -3260084.0248
##     60 754277027.4505            -nan     0.1000 -1635629.2838
##     80 652767126.3607            -nan     0.1000 -2587996.6289
##    100 596812140.8238            -nan     0.1000 -10268817.7717
##    120 542640820.4174            -nan     0.1000 -3771407.3513
##    140 496612492.6886            -nan     0.1000 -2379011.9998
##    150 476510569.4942            -nan     0.1000 -317929.2953
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6015688539.3074            -nan     0.1000 434265283.5254
##      2 5662518644.9517            -nan     0.1000 366728999.0463
##      3 5297257505.7971            -nan     0.1000 300188742.8196
##      4 4957064801.9814            -nan     0.1000 327682490.1510
##      5 4651993370.3552            -nan     0.1000 271131069.5595
##      6 4419584543.4236            -nan     0.1000 229538119.8002
##      7 4157505136.7809            -nan     0.1000 200658671.4111
##      8 3948808718.5255            -nan     0.1000 177832178.4891
##      9 3756103417.3924            -nan     0.1000 176266111.3316
##     10 3574418887.4538            -nan     0.1000 108067123.9794
##     20 2392537603.4594            -nan     0.1000 88514060.8708
##     40 1538723936.0438            -nan     0.1000 21317859.1992
##     60 1256640513.4725            -nan     0.1000 -167548.6880
##     80 1118773089.2890            -nan     0.1000 -6813971.3273
##    100 1039411120.9378            -nan     0.1000 3616882.3391
##    120 984784994.4457            -nan     0.1000 -1187358.5186
##    140 954194805.0250            -nan     0.1000 -8960840.7418
##    150 934512978.6260            -nan     0.1000 1401906.6821
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5839659865.2026            -nan     0.1000 490458306.3422
##      2 5301059301.6303            -nan     0.1000 532753125.2908
##      3 4849270828.8471            -nan     0.1000 403486035.3662
##      4 4425399005.5279            -nan     0.1000 378739007.7588
##      5 4059700879.2591            -nan     0.1000 345901739.8822
##      6 3765759319.8551            -nan     0.1000 237332054.7883
##      7 3515833259.5746            -nan     0.1000 234823899.1492
##      8 3276872813.8491            -nan     0.1000 207786658.7914
##      9 3055575599.4047            -nan     0.1000 220367647.5010
##     10 2872473713.3813            -nan     0.1000 207452922.1768
##     20 1769385707.5505            -nan     0.1000 45397211.2705
##     40 1138157828.8653            -nan     0.1000 -10819644.6710
##     60 941681762.1068            -nan     0.1000 -11134083.1547
##     80 836598045.8169            -nan     0.1000 -10212782.3586
##    100 770765291.4753            -nan     0.1000 -3205441.4567
##    120 722089745.2672            -nan     0.1000 -4445965.9502
##    140 677819016.6476            -nan     0.1000 -2868324.7505
##    150 654195730.3485            -nan     0.1000 -7908829.5296
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5782347524.3524            -nan     0.1000 650371793.5030
##      2 5188760023.5454            -nan     0.1000 540162142.6247
##      3 4713246559.4597            -nan     0.1000 497062567.0849
##      4 4272201009.9585            -nan     0.1000 388093685.1760
##      5 3903510795.4591            -nan     0.1000 359290013.2179
##      6 3564321318.9448            -nan     0.1000 309023707.7738
##      7 3300242795.0271            -nan     0.1000 295073842.6950
##      8 3044960145.2357            -nan     0.1000 223666961.6002
##      9 2804246967.6814            -nan     0.1000 203915922.4443
##     10 2589975872.5504            -nan     0.1000 206975992.2099
##     20 1470214007.9728            -nan     0.1000 42065900.6618
##     40 911763318.4862            -nan     0.1000 5080460.0301
##     60 742436942.7321            -nan     0.1000 -7643676.0366
##     80 642616565.1088            -nan     0.1000 -412982.9438
##    100 579358598.2890            -nan     0.1000 -3876573.5789
##    120 527323069.0771            -nan     0.1000 -2074107.0070
##    140 492295900.1968            -nan     0.1000 -3597403.4363
##    150 475041718.5944            -nan     0.1000 -4638598.0746
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6003148545.5368            -nan     0.1000 440696197.7289
##      2 5618121806.2035            -nan     0.1000 366392849.6558
##      3 5224170672.5765            -nan     0.1000 361658686.2444
##      4 4933495569.9515            -nan     0.1000 305137426.3268
##      5 4631715251.3577            -nan     0.1000 276440603.2351
##      6 4340043716.7738            -nan     0.1000 225547108.1422
##      7 4090404820.5020            -nan     0.1000 226030029.3024
##      8 3872455818.5956            -nan     0.1000 183804381.4153
##      9 3676126741.8468            -nan     0.1000 141746170.3819
##     10 3508445556.3533            -nan     0.1000 158987616.0645
##     20 2367956137.9017            -nan     0.1000 86031425.4321
##     40 1502597310.7965            -nan     0.1000 4115433.5098
##     60 1220187489.2123            -nan     0.1000 -1318855.8802
##     80 1074970595.5351            -nan     0.1000 2965579.1358
##    100 1003656791.3753            -nan     0.1000 -8189501.8325
##    120 956755558.6838            -nan     0.1000 -8869984.2367
##    140 919918348.0513            -nan     0.1000 -1579839.9326
##    150 898859411.7137            -nan     0.1000 -4704408.8991
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5768901813.5977            -nan     0.1000 627836519.7216
##      2 5240718046.4360            -nan     0.1000 423647901.9623
##      3 4775577372.0024            -nan     0.1000 446770169.5178
##      4 4387879994.8199            -nan     0.1000 285394394.8184
##      5 4075233364.2288            -nan     0.1000 216130282.7237
##      6 3795796524.8102            -nan     0.1000 267112607.3085
##      7 3512444381.9844            -nan     0.1000 279801897.1429
##      8 3258677866.8500            -nan     0.1000 256250438.3751
##      9 3052571649.2398            -nan     0.1000 181297324.5578
##     10 2839268670.9120            -nan     0.1000 211453440.1922
##     20 1736846741.0322            -nan     0.1000 54105253.7643
##     40 1078248341.3843            -nan     0.1000 12140673.3381
##     60 883645746.0234            -nan     0.1000 -1112589.9424
##     80 781860330.1106            -nan     0.1000 -11862966.2966
##    100 711181316.4385            -nan     0.1000 -7837990.6921
##    120 665628406.0639            -nan     0.1000 -4395166.4222
##    140 629399141.0209            -nan     0.1000 -148440.3852
##    150 610542717.6065            -nan     0.1000 -4788577.0379
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5751620320.6718            -nan     0.1000 668215597.6215
##      2 5172206585.5886            -nan     0.1000 572925904.5185
##      3 4613787246.1843            -nan     0.1000 422444950.0094
##      4 4143842913.5124            -nan     0.1000 354858762.0584
##      5 3795927438.5854            -nan     0.1000 275781532.8229
##      6 3460142347.0227            -nan     0.1000 331748850.7943
##      7 3142590800.8974            -nan     0.1000 292610081.8179
##      8 2888256912.7457            -nan     0.1000 262227875.9217
##      9 2646521013.4902            -nan     0.1000 198088913.6044
##     10 2451612515.9980            -nan     0.1000 161215385.1770
##     20 1410538397.6859            -nan     0.1000 37892830.2839
##     40 886171624.8191            -nan     0.1000 3670007.1991
##     60 714804969.7231            -nan     0.1000 1162957.6605
##     80 619517452.3084            -nan     0.1000 -2792700.8311
##    100 557144561.1797            -nan     0.1000 -8024288.7576
##    120 515360424.7610            -nan     0.1000 -1765547.8045
##    140 472260135.5726            -nan     0.1000 -6271271.9655
##    150 459948745.0299            -nan     0.1000 -4140900.9787
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6080116818.3424            -nan     0.1000 462920689.0670
##      2 5718627412.4956            -nan     0.1000 340208619.3661
##      3 5353777084.4551            -nan     0.1000 329792436.3414
##      4 4977977569.7284            -nan     0.1000 325165952.1541
##      5 4692671413.8403            -nan     0.1000 280814083.1291
##      6 4439333091.5729            -nan     0.1000 252488911.6995
##      7 4189011279.8147            -nan     0.1000 214073269.5163
##      8 4014023372.0433            -nan     0.1000 113463105.9833
##      9 3813272883.6062            -nan     0.1000 178236166.4268
##     10 3612898933.2786            -nan     0.1000 206562383.0715
##     20 2394397251.5079            -nan     0.1000 69368400.2742
##     40 1530528226.6767            -nan     0.1000 20628485.3485
##     60 1233394964.4762            -nan     0.1000 9586055.8782
##     80 1112242799.6090            -nan     0.1000 2302800.9602
##    100 1047893783.5159            -nan     0.1000 -1831676.5896
##    120 1006688677.0669            -nan     0.1000 -6209632.4181
##    140 966772817.9699            -nan     0.1000 -2076187.0415
##    150 948974224.1447            -nan     0.1000 -14630314.7414
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6000303943.1096            -nan     0.1000 498509054.7949
##      2 5376986731.3652            -nan     0.1000 510378216.5721
##      3 4870500824.1644            -nan     0.1000 465081351.4042
##      4 4441362633.5819            -nan     0.1000 419688864.3220
##      5 4126222050.0349            -nan     0.1000 325721949.7884
##      6 3798763454.5292            -nan     0.1000 334162576.9759
##      7 3510550629.0862            -nan     0.1000 269901878.7145
##      8 3259447521.7684            -nan     0.1000 251396783.0632
##      9 3068517127.4103            -nan     0.1000 158272906.0957
##     10 2867877635.7615            -nan     0.1000 158275084.7516
##     20 1738240305.3569            -nan     0.1000 53032923.6129
##     40 1096773559.7424            -nan     0.1000 8361598.5860
##     60 902202816.7975            -nan     0.1000 -487130.1552
##     80 811443122.5498            -nan     0.1000 -5710798.6224
##    100 750146070.9596            -nan     0.1000 -3887814.5939
##    120 698502911.8275            -nan     0.1000 -2116330.5754
##    140 656902967.7003            -nan     0.1000 -7356327.6513
##    150 645092435.8899            -nan     0.1000 -6239445.1527
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5814572603.9377            -nan     0.1000 682996268.0045
##      2 5213534772.4058            -nan     0.1000 572722404.0068
##      3 4743232100.7752            -nan     0.1000 470016063.9899
##      4 4294117151.1999            -nan     0.1000 415934918.0683
##      5 3910970757.4528            -nan     0.1000 331536640.0875
##      6 3577315944.6631            -nan     0.1000 281135117.4293
##      7 3252368921.0338            -nan     0.1000 308897848.9231
##      8 3013621233.5182            -nan     0.1000 234735703.0026
##      9 2770442415.7970            -nan     0.1000 152555629.7149
##     10 2567408965.2898            -nan     0.1000 188505963.2604
##     20 1490052134.8872            -nan     0.1000 51235170.4764
##     40 941078347.0439            -nan     0.1000 5047527.5306
##     60 751221053.4070            -nan     0.1000 -1097530.2578
##     80 651469291.7804            -nan     0.1000 -2877321.3116
##    100 578342292.1491            -nan     0.1000 -4677671.7035
##    120 525325253.0438            -nan     0.1000 -6571431.5277
##    140 471453662.3423            -nan     0.1000 -2006252.6860
##    150 452214807.5770            -nan     0.1000 -3386295.7265
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6172391947.1586            -nan     0.1000 411251054.4800
##      2 5696752229.9187            -nan     0.1000 498314328.1433
##      3 5353960938.9204            -nan     0.1000 354395476.8698
##      4 5013856954.8120            -nan     0.1000 338913713.3296
##      5 4728720431.4199            -nan     0.1000 281302437.6111
##      6 4468686282.4851            -nan     0.1000 271283423.3398
##      7 4235272267.8623            -nan     0.1000 231675675.8983
##      8 3995703447.1697            -nan     0.1000 181956116.2806
##      9 3790370007.0756            -nan     0.1000 198755178.0477
##     10 3625980449.6807            -nan     0.1000 166023793.1293
##     20 2394866616.2821            -nan     0.1000 77271672.1161
##     40 1463836946.9903            -nan     0.1000 20682126.5241
##     60 1156694466.0220            -nan     0.1000 1420630.2039
##     80 1014288959.2176            -nan     0.1000 885104.5064
##    100 950977111.9951            -nan     0.1000 3668326.2502
##    120 889811254.3538            -nan     0.1000 1459481.5101
##    140 858074617.4842            -nan     0.1000 -15133067.9541
##    150 842502225.8202            -nan     0.1000 -3928169.7886
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5918363044.9584            -nan     0.1000 604381049.2106
##      2 5340248804.3755            -nan     0.1000 581720908.3032
##      3 4895092281.8385            -nan     0.1000 406324332.4276
##      4 4461739100.7458            -nan     0.1000 438131659.1260
##      5 4102085056.4324            -nan     0.1000 300179518.1686
##      6 3790136107.8580            -nan     0.1000 329946149.6718
##      7 3502219197.3189            -nan     0.1000 233046990.6349
##      8 3256201067.0370            -nan     0.1000 264607312.3862
##      9 3052512400.6930            -nan     0.1000 193738204.1323
##     10 2899921588.9719            -nan     0.1000 150857090.4569
##     20 1706513726.9130            -nan     0.1000 35975629.2356
##     40 1025444246.1755            -nan     0.1000 7174803.0967
##     60 837626927.1366            -nan     0.1000 -5065240.8924
##     80 743978793.2639            -nan     0.1000 -3337734.9498
##    100 690480223.0380            -nan     0.1000 -6589558.9514
##    120 650742869.3526            -nan     0.1000 -304704.1013
##    140 614344436.8564            -nan     0.1000 -2811297.0323
##    150 589405228.4681            -nan     0.1000 -2292717.2929
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5804095752.8832            -nan     0.1000 730078416.8591
##      2 5225333786.1990            -nan     0.1000 540583549.1362
##      3 4637499707.5971            -nan     0.1000 462097826.6053
##      4 4190912982.2407            -nan     0.1000 460754355.6754
##      5 3815183057.7995            -nan     0.1000 280118980.6605
##      6 3478613702.9520            -nan     0.1000 316255786.6623
##      7 3160329054.1042            -nan     0.1000 319265728.0363
##      8 2919382571.0227            -nan     0.1000 209883445.6096
##      9 2681588574.3558            -nan     0.1000 232438953.5199
##     10 2480951527.0838            -nan     0.1000 176110026.0942
##     20 1382604245.3122            -nan     0.1000 55003823.6733
##     40 833902816.3544            -nan     0.1000 -3002335.7228
##     60 698815354.7706            -nan     0.1000 -3164541.8754
##     80 621440036.0748            -nan     0.1000 -3151055.9603
##    100 564508924.9198            -nan     0.1000 -8837790.2598
##    120 518215403.4279            -nan     0.1000 -4684376.8726
##    140 479049672.3819            -nan     0.1000 -913742.0487
##    150 463186574.5090            -nan     0.1000 -4875529.4769
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 6147920783.5987            -nan     0.1000 391818554.0390
##      2 5759701488.9908            -nan     0.1000 388088078.7301
##      3 5429352474.1258            -nan     0.1000 336602931.4662
##      4 5105764841.1366            -nan     0.1000 315930903.3408
##      5 4800440118.1854            -nan     0.1000 269569945.2893
##      6 4510229782.1048            -nan     0.1000 266597076.1856
##      7 4239577892.6229            -nan     0.1000 173261508.5707
##      8 3984619003.5126            -nan     0.1000 219104150.9707
##      9 3788076680.6935            -nan     0.1000 185147265.7937
##     10 3599482730.9720            -nan     0.1000 171931242.5041
##     20 2397922702.0765            -nan     0.1000 78752708.8622
##     40 1486476659.3824            -nan     0.1000 19573351.3521
##     60 1215872495.5116            -nan     0.1000 6866562.7921
##     80 1112553238.7515            -nan     0.1000 1493051.3460
##    100 1043944153.8973            -nan     0.1000 1767901.7213
##    120 996959742.2375            -nan     0.1000 1628283.7121
##    140 972127411.2428            -nan     0.1000 -4253596.4131
##    150 953063130.9157            -nan     0.1000 -2739642.2835
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5925009546.4218            -nan     0.1000 569711228.5859
##      2 5407797685.2981            -nan     0.1000 453672869.4688
##      3 4929804061.7367            -nan     0.1000 473961247.6789
##      4 4478379504.1601            -nan     0.1000 427565595.6868
##      5 4107730474.4833            -nan     0.1000 356419809.4052
##      6 3804065347.4720            -nan     0.1000 256512003.4791
##      7 3509543758.0096            -nan     0.1000 272591792.9466
##      8 3281190861.9536            -nan     0.1000 188025403.6709
##      9 3081988815.1138            -nan     0.1000 157076031.1535
##     10 2859120113.6673            -nan     0.1000 178458115.9795
##     20 1719976487.2362            -nan     0.1000 70107244.9089
##     40 1123534925.1787            -nan     0.1000 7349205.0850
##     60 945818162.2569            -nan     0.1000 -9355518.9578
##     80 852515151.1640            -nan     0.1000 -11083434.9810
##    100 782075353.1237            -nan     0.1000 -1755176.9620
##    120 723801490.7290            -nan     0.1000 -691429.9723
##    140 682570410.1870            -nan     0.1000 -5982705.0909
##    150 658866015.7659            -nan     0.1000 -2423459.5125
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5896175321.6603            -nan     0.1000 672929273.3563
##      2 5263758919.3340            -nan     0.1000 612364381.3973
##      3 4761268907.6126            -nan     0.1000 462411702.5635
##      4 4273685370.5568            -nan     0.1000 405580625.2650
##      5 3867658951.3609            -nan     0.1000 370079960.9854
##      6 3536616976.8564            -nan     0.1000 329419009.5395
##      7 3261820957.1270            -nan     0.1000 299379905.8343
##      8 2994744400.9708            -nan     0.1000 221001160.7120
##      9 2747803854.0362            -nan     0.1000 224887766.9828
##     10 2553947316.4261            -nan     0.1000 165269632.6103
##     20 1458747794.9849            -nan     0.1000 55189731.0962
##     40 939500212.8855            -nan     0.1000 550001.4490
##     60 773440319.7295            -nan     0.1000 -5808007.2892
##     80 675574632.9241            -nan     0.1000 -7071363.7227
##    100 596692234.5166            -nan     0.1000 -1493991.8062
##    120 538528256.6339            -nan     0.1000 -5318889.6683
##    140 494825668.5729            -nan     0.1000 -3492791.7826
##    150 478853284.9723            -nan     0.1000 -1772709.9057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1 5717854958.6248            -nan     0.1000 647384629.2667
##      2 5135196344.5461            -nan     0.1000 561323851.6643
##      3 4639991106.2062            -nan     0.1000 482440421.8882
##      4 4237903719.5836            -nan     0.1000 416519229.2280
##      5 3874573747.4656            -nan     0.1000 269917643.7163
##      6 3523440740.1211            -nan     0.1000 325327052.4296
##      7 3205670333.5018            -nan     0.1000 310694170.7426
##      8 2950904221.2388            -nan     0.1000 175817302.7315
##      9 2689527803.6894            -nan     0.1000 222614049.2454
##     10 2492069747.8062            -nan     0.1000 150621241.2042
##     20 1417436674.6797            -nan     0.1000 46224893.3319
##     40 897393789.3868            -nan     0.1000 -2797134.2295
##     60 736500028.2566            -nan     0.1000 1938194.0520
##     80 647526948.3527            -nan     0.1000 -4429868.8365
##    100 596957994.3258            -nan     0.1000 -896000.7551
##    120 547302725.9249            -nan     0.1000 -3157075.0850
##    140 505460775.8940            -nan     0.1000 -2596057.2899
##    150 487750801.8779            -nan     0.1000 -8510460.8780
predictions.gbm <- predict(gbm, housing.test, na.action = na.pass)
RMSE(predictions.gbm, housing.test$SalePrice)
## [1] 26147.59

RMSE 26147.59

16.

set.seed(1)
svmlin <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "svmLinear", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(C = c(1, 190, 225)))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
predictions.svmlin <- predict(svmlin, housing.test, na.action = na.pass)
RMSE(predictions.svmlin, housing.test$SalePrice)
## [1] 26994.74

RMSE = 26994.74 C controls how big the penalty there is for the “soft margin” larger value = thinner margins.

17.

set.seed(1)
svmrad <- train(SalePrice ~ ., data= housing.train, preProc = "knnImpute", na.action = na.pass, method = "svmRadial", trControl = trainControl("cv", number = 10))
## Warning in preProcess.default(method = "knnImpute", k = 5, x = structure(c(60, :
## These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stCBlock, Exterior1stImStucc, Exterior2ndCBlock,
## ElectricalMix, MiscFeatureTenC
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlMembran,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc, Exterior1stStone
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition1RRNe, Condition2PosN,
## Condition2RRNn, RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAe, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, HeatingQCPo
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRAn, Condition2RRNn,
## RoofMatlRoll, Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: UtilitiesNoSeWa, Condition2PosA,
## Condition2RRNn, RoofMatlMetal, RoofMatlRoll, Exterior1stAsphShn,
## Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, Exterior2ndOther, ExterCondPo,
## SaleTypeCon
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc, FunctionalSev
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: Condition2RRNn, RoofMatlRoll,
## Exterior1stAsphShn, Exterior1stImStucc
## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
predictions.svmrad <- predict(svmrad, housing.test, na.action = na.pass)
RMSE(predictions.svmrad, housing.test$SalePrice)
## [1] 77423.92

RMSE = 77423.92

18.

compare = resamples(list(L=lasso, R=ridge, E=enet, RF=rf, svmLIN=svmlin, svmRAD=svmrad, G=gbm))
summary(compare, metric=compare$metrics)
## 
## Call:
## summary.resamples(object = compare, metric = compare$metrics)
## 
## Models: L, R, E, RF, svmLIN, svmRAD, G 
## Number of resamples: 10 
## 
## MAE 
##            Min.  1st Qu.   Median     Mean  3rd Qu.     Max. NA's
## L      14701.66 16660.84 18364.14 18250.12 20141.77 21956.57    0
## R      15137.68 18273.20 19050.04 18941.71 20914.96 21819.43    0
## E      15137.68 18273.20 19050.04 18941.71 20914.96 21819.43    0
## RF     16072.94 16645.57 17794.08 17965.57 19094.18 20601.00    0
## svmLIN 13841.20 15698.66 16950.14 17046.90 18694.84 20207.97    0
## svmRAD 50827.34 53920.22 57266.55 55874.01 58091.90 59037.47    0
## G      15872.90 18882.69 19504.85 19386.12 20439.28 22806.77    0
## 
## RMSE 
##            Min.  1st Qu.   Median     Mean  3rd Qu.     Max. NA's
## L      20591.06 24655.50 29433.59 34129.47 37582.84 73690.11    0
## R      21174.89 26115.57 30206.80 34026.59 36962.98 68921.75    0
## E      21174.89 26115.57 30206.80 34026.59 36962.98 68921.75    0
## RF     22847.42 25335.14 28865.13 30461.72 34569.96 43917.08    0
## svmLIN 19190.40 22656.05 26584.85 30830.04 32740.14 63000.04    0
## svmRAD 72099.78 73747.36 81362.70 81830.12 89588.79 94405.31    0
## G      21963.15 25560.58 31139.04 31238.51 35515.43 45284.12    0
## 
## Rsquared 
##             Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## L      0.4493449 0.8126273 0.8847586 0.8246095 0.8929485 0.9293024    0
## R      0.4817790 0.8201216 0.8739816 0.8256416 0.8841331 0.9314466    0
## E      0.4817790 0.8201216 0.8739816 0.8256416 0.8841331 0.9314466    0
## RF     0.7532677 0.8562373 0.8856585 0.8632701 0.9022848 0.9202865    0
## svmLIN 0.4946516 0.8797603 0.8949264 0.8528318 0.9197926 0.9420518    0
## svmRAD 0.2436634 0.4214664 0.4600261 0.4326348 0.4890375 0.5270952    0
## G      0.7421612 0.8386198 0.8650415 0.8495071 0.8947391 0.9078028    0

Random forest had the best RMSE, but it was a narrow victory over the GBM model and the SVMLinear model.

19.

set.seed(123)
in_train <- createDataPartition(housing.train$SalePrice, p = .90, list = FALSE)
train <- housing.train[in_train, ]
val <- housing.train[-in_train, ]

20.

library("RANN")
preproc <- preProcess(train, method="knnImpute")
train.imputed <- predict(preproc, train)
test.imputed <- predict(preproc, housing.test)
val.imputed <- predict(preproc, val)

21.

library(mltools)
## 
## Attaching package: 'mltools'
## The following object is masked from 'package:tidyr':
## 
##     replace_na
library(data.table)
train.onehot <- as.data.frame(one_hot(as.data.table(train.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
val.onehot <- as.data.frame(one_hot(as.data.table(val.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
test <- as.data.frame(one_hot(as.data.table(test.imputed), dropCols = TRUE, dropUnusedLevels = FALSE))
train.onehot <- train.onehot[ , -which(names(train.onehot) %in% "SalePrice")]
val.onehot <- val.onehot[ , -which(names(val.onehot) %in% "SalePrice")]
test <- test[ , -which(names(test) %in% "SalePrice")]
train.labels <- log(train$SalePrice)
val.labels <- log(val$SalePrice)
test_labels <- log(housing.test$SalePrice)

22.

library(tfruns)
library(keras)
set.seed(1)
tensorflow::set_random_seed(1)
## Loaded Tensorflow version 2.8.0
housing_runs <- tuning_run("housing_tuning.R",
                   flags = list(
                   nodes = c(32, 64, 128, 392),
                   learning_rate = c(0.01, 0.05, 0.001, 0.0001),
                   batch_size=c(50, 100, 500, 1000),
                   epochs=c(30, 50, 100, 200),
                   activation=c("relu","sigmoid","tanh"),
                   dropout1=c(.2, .3, .5),
                   dropout2=c(.2, .4, .5)
                   ), sample = .02)
## 6,912 total combinations of flags
## (sampled to 139 combinations)
## Training run 1/139 (flags = list(32, 0.001, 1000, 200, "relu", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-22-55Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-22-55Z
## Training run 2/139 (flags = list(128, 0.05, 500, 100, "relu", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-23-17Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-23-17Z
## Training run 3/139 (flags = list(32, 0.01, 50, 100, "tanh", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-23-34Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-23-34Z
## Training run 4/139 (flags = list(64, 0.01, 500, 100, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-24-03Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-24-03Z
## Training run 5/139 (flags = list(32, 1e-04, 1000, 200, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-24-19Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-24-19Z
## Training run 6/139 (flags = list(128, 0.05, 100, 200, "tanh", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-24-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-24-39Z
## Training run 7/139 (flags = list(128, 0.001, 500, 30, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-25-06Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-25-06Z
## Training run 8/139 (flags = list(64, 1e-04, 50, 30, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-25-17Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-25-17Z
## Training run 9/139 (flags = list(64, 0.01, 100, 200, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-25-30Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-25-30Z
## Training run 10/139 (flags = list(128, 0.001, 1000, 100, "tanh", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-25-56Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-25-56Z
## Training run 11/139 (flags = list(128, 0.01, 1000, 30, "sigmoid", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-26-16Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-26-16Z
## Training run 12/139 (flags = list(32, 0.01, 500, 200, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-26-29Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-26-29Z
## Training run 13/139 (flags = list(32, 0.05, 100, 50, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-26-51Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-26-51Z
## Training run 14/139 (flags = list(32, 0.05, 100, 30, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-27-05Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-27-05Z
## Training run 15/139 (flags = list(64, 0.05, 1000, 100, "tanh", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-27-17Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-27-17Z
## Training run 16/139 (flags = list(64, 0.001, 50, 50, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-27-32Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-27-32Z
## Training run 17/139 (flags = list(128, 0.05, 50, 30, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-27-47Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-27-47Z
## Training run 18/139 (flags = list(32, 0.001, 50, 50, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-28-00Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-28-00Z
## Training run 19/139 (flags = list(128, 1e-04, 50, 50, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-28-15Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-28-15Z
## Training run 20/139 (flags = list(32, 0.05, 100, 200, "relu", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-28-31Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-28-31Z
## Training run 21/139 (flags = list(32, 0.05, 500, 30, "relu", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-28-57Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-28-57Z
## Training run 22/139 (flags = list(32, 0.001, 500, 50, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-29-08Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-29-08Z
## Training run 23/139 (flags = list(32, 0.001, 100, 200, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-29-22Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-29-22Z
## Training run 24/139 (flags = list(64, 1e-04, 500, 50, "relu", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-29-48Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-29-48Z
## Training run 25/139 (flags = list(32, 0.05, 500, 200, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-30-02Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-30-02Z
## Training run 26/139 (flags = list(64, 0.01, 500, 30, "tanh", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-30-24Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-30-24Z
## Training run 27/139 (flags = list(64, 1e-04, 1000, 50, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-30-35Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-30-35Z
## Training run 28/139 (flags = list(392, 0.001, 500, 100, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-30-48Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-30-48Z
## Training run 29/139 (flags = list(128, 1e-04, 50, 200, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-31-07Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-31-07Z
## Training run 30/139 (flags = list(32, 0.01, 500, 30, "tanh", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-31-40Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-31-40Z
## Training run 31/139 (flags = list(392, 0.01, 100, 50, "tanh", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-31-52Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-31-52Z
## Training run 32/139 (flags = list(64, 0.05, 50, 50, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-32-08Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-32-08Z
## Training run 33/139 (flags = list(64, 0.001, 50, 50, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-32-23Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-32-23Z
## Training run 34/139 (flags = list(64, 0.001, 500, 30, "relu", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-32-43Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-32-43Z
## Training run 35/139 (flags = list(64, 0.05, 500, 100, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-32-55Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-32-55Z
## Training run 36/139 (flags = list(128, 1e-04, 500, 50, "tanh", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-33-11Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-33-11Z
## Training run 37/139 (flags = list(392, 0.01, 100, 100, "sigmoid", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-33-26Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-33-26Z
## Training run 38/139 (flags = list(392, 0.001, 100, 100, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-33-56Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-33-56Z
## Training run 39/139 (flags = list(392, 0.01, 100, 30, "sigmoid", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-34-26Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-34-26Z
## Training run 40/139 (flags = list(64, 0.001, 1000, 200, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-34-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-34-39Z
## Training run 41/139 (flags = list(392, 0.001, 500, 30, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-35-00Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-35-00Z
## Training run 42/139 (flags = list(128, 0.05, 100, 50, "tanh", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-35-15Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-35-15Z
## Training run 43/139 (flags = list(64, 0.05, 50, 50, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-35-29Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-35-29Z
## Training run 44/139 (flags = list(392, 0.05, 500, 30, "sigmoid", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-35-44Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-35-44Z
## Training run 45/139 (flags = list(32, 0.001, 100, 30, "relu", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-35-59Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-35-59Z
## Training run 46/139 (flags = list(128, 0.05, 1000, 50, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-36-11Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-36-11Z
## Training run 47/139 (flags = list(392, 0.05, 1000, 200, "sigmoid", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-36-26Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-36-26Z
## Training run 48/139 (flags = list(64, 0.05, 50, 200, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-36-54Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-36-54Z
## Training run 49/139 (flags = list(64, 0.001, 1000, 50, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-37-44Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-37-44Z
## Training run 50/139 (flags = list(128, 0.05, 500, 30, "tanh", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-37-58Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-37-58Z
## Training run 51/139 (flags = list(128, 0.01, 1000, 100, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-38-10Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-38-10Z
## Training run 52/139 (flags = list(64, 0.001, 500, 30, "tanh", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-38-27Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-38-27Z
## Training run 53/139 (flags = list(64, 0.05, 50, 100, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-38-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-38-39Z
## Training run 54/139 (flags = list(392, 0.05, 100, 30, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-39-09Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-39-09Z
## Training run 55/139 (flags = list(392, 1e-04, 100, 100, "relu", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-39-23Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-39-23Z
## Training run 56/139 (flags = list(64, 1e-04, 50, 30, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-39-45Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-39-45Z
## Training run 57/139 (flags = list(64, 0.01, 50, 100, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-39-58Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-39-58Z
## Training run 58/139 (flags = list(32, 1e-04, 500, 30, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-40-19Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-40-19Z
## Training run 59/139 (flags = list(64, 0.01, 100, 100, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-40-31Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-40-31Z
## Training run 60/139 (flags = list(64, 0.05, 100, 30, "relu", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-40-50Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-40-50Z
## Training run 61/139 (flags = list(64, 1e-04, 50, 200, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-41-02Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-41-02Z
## Training run 62/139 (flags = list(64, 0.05, 500, 200, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-41-35Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-41-35Z
## Training run 63/139 (flags = list(32, 0.01, 50, 200, "relu", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-42-05Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-42-05Z
## Training run 64/139 (flags = list(128, 0.01, 1000, 50, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-42-37Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-42-37Z
## Training run 65/139 (flags = list(128, 0.01, 1000, 200, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-42-51Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-42-51Z
## Training run 66/139 (flags = list(392, 0.05, 500, 50, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-43-14Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-43-14Z
## Training run 67/139 (flags = list(128, 0.05, 100, 200, "relu", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-43-28Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-43-28Z
## Training run 68/139 (flags = list(128, 1e-04, 1000, 200, "relu", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-43-57Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-43-57Z
## Training run 69/139 (flags = list(392, 0.001, 500, 200, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-44-20Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-44-20Z
## Training run 70/139 (flags = list(64, 0.05, 50, 50, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-44-49Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-44-49Z
## Training run 71/139 (flags = list(392, 0.05, 500, 50, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-45-05Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-45-05Z
## Training run 72/139 (flags = list(128, 0.05, 500, 50, "relu", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-45-20Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-45-20Z
## Training run 73/139 (flags = list(128, 0.001, 50, 50, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-45-34Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-45-34Z
## Training run 74/139 (flags = list(32, 0.01, 100, 200, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-45-50Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-45-50Z
## Training run 75/139 (flags = list(392, 0.01, 500, 200, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-46-17Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-46-17Z
## Training run 76/139 (flags = list(32, 1e-04, 50, 30, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-46-46Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-46-46Z
## Training run 77/139 (flags = list(392, 1e-04, 500, 100, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-47-01Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-47-01Z
## Training run 78/139 (flags = list(128, 0.05, 100, 30, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-47-20Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-47-20Z
## Training run 79/139 (flags = list(64, 1e-04, 500, 50, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-47-33Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-47-33Z
## Training run 80/139 (flags = list(392, 0.01, 100, 50, "relu", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-47-46Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-47-46Z
## Training run 81/139 (flags = list(128, 0.05, 500, 50, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-48-07Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-48-07Z
## Training run 82/139 (flags = list(32, 1e-04, 100, 30, "relu", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-48-21Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-48-21Z
## Training run 83/139 (flags = list(32, 1e-04, 50, 100, "tanh", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-48-34Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-48-34Z
## Training run 84/139 (flags = list(392, 0.001, 1000, 200, "tanh", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-49-04Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-49-04Z
## Training run 85/139 (flags = list(64, 0.05, 100, 100, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-49-35Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-49-35Z
## Training run 86/139 (flags = list(128, 0.001, 100, 200, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-49-55Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-49-55Z
## Training run 87/139 (flags = list(392, 0.001, 500, 50, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-50-24Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-50-24Z
## Training run 88/139 (flags = list(392, 1e-04, 500, 30, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-50-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-50-39Z
## Training run 89/139 (flags = list(32, 1e-04, 500, 30, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-50-52Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-50-52Z
## Training run 90/139 (flags = list(32, 0.05, 100, 100, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-51-04Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-51-04Z
## Training run 91/139 (flags = list(128, 1e-04, 100, 30, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-51-22Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-51-22Z
## Training run 92/139 (flags = list(64, 0.05, 500, 50, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-51-35Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-51-35Z
## Training run 93/139 (flags = list(32, 0.01, 100, 100, "relu", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-51-47Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-51-47Z
## Training run 94/139 (flags = list(32, 0.001, 50, 50, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-52-05Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-52-05Z
## Training run 95/139 (flags = list(128, 0.05, 500, 200, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-52-20Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-52-20Z
## Training run 96/139 (flags = list(64, 0.05, 1000, 50, "tanh", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-52-50Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-52-50Z
## Training run 97/139 (flags = list(64, 0.01, 1000, 200, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-53-05Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-53-05Z
## Training run 98/139 (flags = list(64, 0.001, 100, 50, "relu", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-53-27Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-53-27Z
## Training run 99/139 (flags = list(392, 1e-04, 500, 30, "tanh", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-53-41Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-53-41Z
## Training run 100/139 (flags = list(128, 0.001, 500, 50, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-53-57Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-53-57Z
## Training run 101/139 (flags = list(392, 1e-04, 1000, 30, "relu", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-54-10Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-54-10Z
## Training run 102/139 (flags = list(64, 1e-04, 100, 200, "relu", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-54-26Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-54-26Z
## Training run 103/139 (flags = list(128, 0.05, 500, 50, "relu", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-54-53Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-54-53Z
## Training run 104/139 (flags = list(392, 0.001, 1000, 30, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-55-08Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-55-08Z
## Training run 105/139 (flags = list(128, 0.01, 1000, 30, "tanh", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-55-23Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-55-23Z
## Training run 106/139 (flags = list(32, 1e-04, 100, 200, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-55-34Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-55-34Z
## Training run 107/139 (flags = list(64, 0.01, 500, 30, "relu", 0.3, 0.5))
## Using run directory runs/2022-04-19T14-56-01Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-56-01Z
## Training run 108/139 (flags = list(64, 0.001, 50, 100, "tanh", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-56-13Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-56-13Z
## Training run 109/139 (flags = list(32, 0.01, 50, 100, "relu", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-56-36Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-56-36Z
## Training run 110/139 (flags = list(128, 0.001, 500, 30, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-57-06Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-57-06Z
## Training run 111/139 (flags = list(128, 0.001, 1000, 100, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T14-57-18Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-57-18Z
## Training run 112/139 (flags = list(64, 0.001, 100, 30, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T14-57-35Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-57-35Z
## Training run 113/139 (flags = list(128, 1e-04, 50, 100, "sigmoid", 0.3, 0.2))
## Using run directory runs/2022-04-19T14-57-48Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-57-48Z
## Training run 114/139 (flags = list(64, 0.001, 1000, 100, "sigmoid", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-58-10Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-58-10Z
## Training run 115/139 (flags = list(32, 1e-04, 100, 30, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T14-58-26Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-58-26Z
## Training run 116/139 (flags = list(392, 0.05, 100, 100, "relu", 0.5, 0.4))
## Using run directory runs/2022-04-19T14-58-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-58-39Z
## Training run 117/139 (flags = list(64, 0.001, 500, 100, "relu", 0.5, 0.2))
## Using run directory runs/2022-04-19T14-59-01Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-59-01Z
## Training run 118/139 (flags = list(32, 0.05, 1000, 200, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T14-59-18Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-59-18Z
## Training run 119/139 (flags = list(392, 0.001, 50, 200, "tanh", 0.3, 0.4))
## Using run directory runs/2022-04-19T14-59-49Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T14-59-49Z
## Training run 120/139 (flags = list(128, 1e-04, 1000, 200, "sigmoid", 0.5, 0.5))
## Using run directory runs/2022-04-19T15-00-31Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-00-31Z
## Training run 121/139 (flags = list(392, 0.05, 100, 30, "tanh", 0.2, 0.2))
## Using run directory runs/2022-04-19T15-00-53Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-00-53Z
## Training run 122/139 (flags = list(392, 0.001, 100, 200, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T15-01-06Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-01-06Z
## Training run 123/139 (flags = list(64, 0.05, 100, 200, "relu", 0.3, 0.4))
## Using run directory runs/2022-04-19T15-01-57Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-01-57Z
## Training run 124/139 (flags = list(128, 0.01, 500, 100, "tanh", 0.3, 0.4))
## Using run directory runs/2022-04-19T15-02-23Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-02-23Z
## Training run 125/139 (flags = list(128, 0.001, 500, 30, "tanh", 0.2, 0.5))
## Using run directory runs/2022-04-19T15-02-40Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-02-40Z
## Training run 126/139 (flags = list(32, 0.01, 50, 30, "tanh", 0.2, 0.5))
## Using run directory runs/2022-04-19T15-02-51Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-02-51Z
## Training run 127/139 (flags = list(32, 1e-04, 100, 30, "relu", 0.2, 0.2))
## Using run directory runs/2022-04-19T15-03-04Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-03-04Z
## Training run 128/139 (flags = list(64, 1e-04, 50, 50, "tanh", 0.3, 0.4))
## Using run directory runs/2022-04-19T15-03-17Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-03-17Z
## Training run 129/139 (flags = list(64, 0.05, 100, 100, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T15-03-32Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-03-32Z
## Training run 130/139 (flags = list(64, 0.05, 50, 50, "sigmoid", 0.2, 0.5))
## Using run directory runs/2022-04-19T15-03-51Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-03-51Z
## Training run 131/139 (flags = list(392, 0.001, 100, 30, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T15-04-11Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-04-11Z
## Training run 132/139 (flags = list(392, 0.01, 1000, 50, "sigmoid", 0.3, 0.5))
## Using run directory runs/2022-04-19T15-04-25Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-04-25Z
## Training run 133/139 (flags = list(32, 0.001, 500, 200, "tanh", 0.5, 0.2))
## Using run directory runs/2022-04-19T15-04-40Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-04-40Z
## Training run 134/139 (flags = list(32, 0.05, 500, 30, "tanh", 0.3, 0.2))
## Using run directory runs/2022-04-19T15-05-10Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-05-10Z
## Training run 135/139 (flags = list(32, 1e-04, 1000, 30, "relu", 0.2, 0.5))
## Using run directory runs/2022-04-19T15-05-22Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-05-22Z
## Training run 136/139 (flags = list(64, 1e-04, 500, 100, "sigmoid", 0.5, 0.2))
## Using run directory runs/2022-04-19T15-05-33Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-05-33Z
## Training run 137/139 (flags = list(32, 0.01, 1000, 50, "sigmoid", 0.2, 0.2))
## Using run directory runs/2022-04-19T15-05-50Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-05-50Z
## Training run 138/139 (flags = list(128, 0.01, 50, 200, "relu", 0.2, 0.4))
## Using run directory runs/2022-04-19T15-06-04Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-06-04Z
## Training run 139/139 (flags = list(64, 0.05, 100, 50, "sigmoid", 0.5, 0.4))
## Using run directory runs/2022-04-19T15-06-39Z
## 
## > FLAGS <- flags(flag_numeric("nodes", 128), flag_numeric("batch_size", 
## +     100), flag_string("activation", "relu"), flag_numeric("learning_rate",  .... [TRUNCATED] 
## 
## > model = keras_model_sequential()
## 
## > model %>% layer_dense(units = FLAGS$nodes, activation = FLAGS$activation, 
## +     input_shape = ncol(train.onehot)) %>% layer_dropout(FLAGS$dropout1) .... [TRUNCATED] 
## 
## > model %>% compile(optimizer = optimizer_adam(learning_rate = FLAGS$learning_rate), 
## +     loss = "mse", metrics = "mae")
## 
## > model %>% fit(as.matrix(train.onehot), train.labels, 
## +     epochs = FLAGS$epochs, batch_size = FLAGS$batch_size, validation_data = list(as.matrix(v .... [TRUNCATED]
## 
## Run completed: runs/2022-04-19T15-06-39Z

23.

housing_runs_ordered <- housing_runs[order(housing_runs$metric_val_loss), ]
head(housing_runs_ordered)
## Data frame: 6 x 27 
##                       run_dir metric_loss metric_mae metric_val_loss
## 2   runs/2022-04-19T15-06-04Z      0.1501     0.3062          0.0158
## 109 runs/2022-04-19T14-31-52Z      0.2980     0.4348          0.0159
## 101 runs/2022-04-19T14-34-26Z      0.5177     0.5669          0.0183
## 21  runs/2022-04-19T14-59-49Z      0.2772     0.4241          0.0198
## 102 runs/2022-04-19T14-33-56Z      0.2688     0.4159          0.0199
## 17  runs/2022-04-19T15-01-57Z      0.0395     0.1395          0.0207
##     metric_val_mae
## 2           0.0924
## 109         0.0919
## 101         0.1006
## 21          0.1046
## 102         0.1064
## 17          0.1061
## # ... with 22 more columns:
## #   flag_nodes, flag_batch_size, flag_activation, flag_learning_rate,
## #   flag_epochs, flag_dropout1, flag_dropout2, epochs, epochs_completed,
## #   metrics, model, loss_function, optimizer, learning_rate, script, start,
## #   end, completed, output, source_code, context, type
view_run(housing_runs$run_dir[2])
## starting httpd help server ... done
## Warning in readLines(file.path(source_dir, file)): incomplete final line found
## on '/tmp/RtmpVCtGOJ/file3699f4655ea331/source/housing_tuning.R'

The best model was run #2 with a val_loss of .0158. The model is a pretty good fit. Not excessively overfitting or underfitting. loss and validation loss appear to be decreasing and converging together in the graph.

The hyper parameters are: nodes = 128, batch_size = 50, activation = relu, learning rate = .01, epochs = 200, dropout1 = .2, dropout 2 = .4

24.

# combine train w/ validation
housing_train <- rbind(train.onehot, val.onehot)
housing_train_labels <- c(train.labels, val.labels)

set.seed(1)
tensorflow::set_random_seed(1)
best_model = keras_model_sequential()
best_model %>%
  layer_dense(units = 128, activation = "relu", input_shape = ncol(housing_train)) %>%
  layer_dropout(.2) %>%
  layer_dense(units = 128, activation = "relu") %>%
  layer_dropout(.4) %>%
  layer_dense(units = 1)

best_model %>% compile(
  optimizer = optimizer_adam(learning_rate=.01),
  loss = 'mse',
  metrics = 'mae')

best_model %>% fit(
  as.matrix(housing_train), housing_train_labels, epochs = 200, 
  batch_size = 50, validation_data=list(as.matrix(test), test_labels))

25.

predictions.nn <- best_model %>% predict(as.matrix(test))

RMSE(exp(predictions.nn), housing.test$SalePrice)
## [1] 47067.6

RMSE = 47067.6

26.

RMSE Comparison Lasso - 34113.53 Ridge - 32406.35 Elastic Net - 32406.35 Random Forest - 26146.37 GBM - 26147.59 svmLinear - 26994.74 svmRadial - 77423.92 Neural Network - 47067.6

The random forest model performed best on this dataset, but the GBM model was very close.